Dissertations / Theses on the topic 'HVASCR'

The thesis introduces new semiconductor structures that are used as protections against Electrostatic Discharge occuring in integrated circuits. The fundamental structure for modeling and simulation has been lateral Silicon Controlled Rectifier. This SCR structure has been modificated to enable tuning of the triggering and holding voltages by changing geometrical mask dimensions. On the base of modeling and simulation the new proposed structures have been published. Also several protection structures have been designed to be manufactured and measured on a testchip. The final electrical behavior has been verified by measurement. Finally, the focus has been aided to protection circuit with bipolar transistor. This approach has been also simulated and verified by measurement. Advantages and disadvantages of the proposed protection structures are commented in the thesis.

It is of interest for companies to keep the annual operating cost of their buildings as low as possible. A substantial share of the annual operating costs are due to the large amount of energy needed for heating of the ventilated air and heating of the rooms inside the buildings. Also much of the electrical energy in the world today is created using fossil fuel or charcoal. This has an environmental aspect and the consumers of energy becomes more and more aware of this. Thus reducing the energy used by a buildings HVAC system can save the users for considerable expenditures and also has an environmental aspect.To find an estimate of the energy consumption a mathematical model representing a building and its HVAC system have been made. This model has been made up of several smaller models representing each component present in the building. These models have then been implemented in verb|Simulink| and the response of the system has been simulated for different scenarios. From these simulations the energy consumption has been extracted and compared to each other. Thus the amount of energy saved for each scenario has been found. The models include two type of controllers to see whether or not the choice of controller design affects the energy efficiency of the system. These two controller designs are the PID controller and the MPC control scheme. Also a discretized and simplified model of the building to be used together with the MPC controller has been found using system identification. In addition to this a Kalman filter that estimates unknown states and filter out disturbances are included in the MPC control scheme.The results from the simulations using a PID controller indicates a possible annual saving of substantial amounts. Thus this report shows that the annual energy consumption in a building can be greatly reduced by introducing simple and relatively cheap modifications to the HVAC system. The results from the simulations using the MPC scheme indicates that even more energy can be saved using this advanced control scheme. However, in order to verify this the MPC controller needs to be fine tuned and several more experiments needs to be reviewed.

Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation.
Doctor of Philosophy
With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.

Dynamic simulations have been successfully applied to the modelling of building heating, ventilating and air-conditioning (HVAC) plant operation. These simulations are generally driven using time-series data as input. Whilst time-series simulations are effective, they tend to be expensive in terms of computer execution time. A possible method for reducing simulation time is to develop a probabilistic picture of the model, by characterising the model as being in one of several states. By determining the probability for being in each model state, predictions of long-term values of quantities of interest can then be obtained using ensemble averages. This study aims to investigate the applicability of the Markov modelling method for the above stated purpose in the simulation of HVAC systems. In addition, the questions of the degree of accuracy which can be expected, and the amount of time-savings which are possible are investigated. The investigation has found that the Markov modelling technique can be successfully applied to simulations of HVAC systems, but that assumptions commonly made concerning the independence of driving variables may often not be appropriate. An alternative approach to implementing the Markov method, taking into Z): account dependencies between driving variables is suggested, but requires further development to be fully effective. The accuracy of results has been found to be related to the sizes of the partial derivatives of the calculated quantity with respect to each of the variables on which it depends, the sizes of the variables' ranges, and the number of states assigned to each variable in developing the probabilistic picture of the model's state. A deterministic error bound for results from Markov simulations is also developed, based on these findings.

Currently heating, ventilation and air conditioning (HVAC) systems are difficult and costly to monitor for energy efficiency performance and reliability. As buildings evolve, they will require higher levels of insulation and air tightness which will require ventilation systems to provide the minimum number of air changes and reduced energy usage by recovering heat from the air before it is expelled. This will necessitate the need for monitoring of the operating performance of these systems so that air quality or building energy efficiency is not detrimentally affected. A typical duct airflow monitoring device uses a pressure differential method to determine the airflow rate but they are fragile, expensive and create an additional pressure loss. The monitoring of airflow rates can indicate problems in the design, installation and operation of a HVAC system. One of the possible alternatives to using pressure differential type devices such as Pitot tube/arrays, orifice plates and Venturis is to use an ultrasonic flow rate sensor, but historically their high cost has restricted their use in HVAC systems. This project has looked at improving on existing measuring systems by developing an ultrasonic in-duct flowmeter system to measure the mean airflow, temperature and humidity of a ventilation duct so that a comparative energy level can be accurately deduced. A proof of concept in-duct ultrasonic airflow monitoring device has been developed and has obtained results within ±3.5% RMS of a Venturi airflow measuring device. Matlab code for a Monte Carlo acoustic ray/particle tracing ultrasonic flowmeter simulation has been developed to study the effects of non-ideal installation scenarios. The fully developed centreline computational fluid dynamics (CFD) mean flow velocity to duct total mean flow velocity error can be up to 13%. Analysis of the CFD data for various duct scenarios has shown that this could be reduced to below 5% by using a transducer offset of approximately ±0.25 duct diameters or widths from the centreline at distances as close as one duct hydraulic diameter from an upstream disturbance, such as caused by a bend.

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.
M.S.
The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

Rapporten behandlar simuleringar, beräkningar och mätningar av HVAC-kablar. En genomgång presenteras av kabelteori samt de olika magnetiska och elektriska effekterna som påverkar skärmströmmarnas storlek.
This report comprises simulations, calculations and measurements of HVAC cables. An introduction to cable theory and a review of the different magnetic and electric effects that has an impact on the amplitude of the screen currents in these cables.

Det är sedan tidigare allmänt känt att när temperaturen sänks på luft med en hög luftfuktighet så fälls det ut kondenserat vatten som en restprodukt. Ombord på stora kryssningsfartyg med många passagerare så är behovet av att kyla luften stort på grund av att passagerarna vill ha en behaglig inomhusmiljö, vilket leder till att det i fartygens stora luftkonditioneringsanläggningar bildas mycket vatten när luft kyls. Den här studien syftar till att undersöka om det går att ta tillvara på det vatten som bildas som en restprodukt i luftkonditioneringsanläggningarna. För att ta reda på om kvalitén och kvantiteten var tillräcklig för att det skulle gå att använda vattnet ur en luftkonditioneringsanläggning så togs ett vattenprov från en luftavfuktare. Detta vattenprov analyserades av ett utav Livsmedelverket ackrediterat laboratorium. För att undersöka hur mycket vatten som bildas, kontaktades en stor leverantör av luftkonditioneringsanläggningar för beräkningsunderlag på ett stort kryssningsfartyg. Övrig information har inhämtats genom litteraturstudier och intervjuer. Studien visar på att det är möjligt att använda kondenserat vatten ifrån luftkonditioneringsanläggningar till dricksvatten och tekniskt vatten. I vissa fall motsvarade vattenproduktionen 10-15 % av det totala vattenbehovet ombord på ett stort kryssningsfartyg. Dock visar också studien på att vattnet måste genomgå olika behandlingar innan det går att använda som dricksvatten alternativt tekniskt vatten.

Heating, Ventilation and Air Conditioning (HVAC) systems consist of all the equipment that control the conditions and distribution of indoor air. Indoor air must be confortable and healthy for the occupants to maximize their productivity. Moreover, HVAC energy consumption is between 20% and 40% of the total energy consumption in developed countries and accounts around 33% of the global CO 2emissions. So the study of HVAC systems plays an important role in building science. The aim of this project is to identify mathematical models that will be employed by intelligent control algorithms which guarantee human comfort indoors, energy saving and less CO 2emissions at the same time. Three models, based on first-principle physical knowledge, are proposed for CO 2concentration, temperature and humidity, respectively, for a room in the Q-building at KTH. Thermodynamic equations and an original estimation of the number of the occupiers of the room are employed. Validation shows that models have really good performances, even with a short training dataset. Discussions on the obtained results are given and some ideas for future work are proposed.

The workable design of HVAC (Heating Ventilating and Air Conditioning) systems is based upon sizing the components individually to meet a peak duty of a nominal operating point. Growing economic pressure demands more cost effective and efficient designs, but the appraisal of alternative solutions is limited by short design and construction times. The design of HVAC systems can benefit from the application of numerical optimisation methods as these allow the rapid appraisal of alternative schemes and the sizing of the components simultaneously for criteria such as minimum first cost, operating cost, life—cycle cost or primary energy consumption. Optimisation problems can be categorised according to the characteristics of the functions used to appraise the solutions and those of the constraints on the problem. This thesis discusses the formulation of EVAC system design problems in this context and describes the development of an optimisation procedure which is based upon a data base of manufactured components and operating parameters such as controller setpoints, mass flow rates and temperatures. The thesis describes several objective functions used in the appraisal of solutions and describes the use of constraint functions in restricting the solution to a practicable design. UVAC system optimised design problems can be solved using direct search methods. The implementation of three direct search algorithms is described and the limitations of each discussed. Conclusions are drawn and the characteristics of HVAC system optimised design problems used to make recommendations for the future development of an idealized algorithm. The thesis describes the development and structure of the optimised design program and its integration with an existing suite of simulation programs. The application of the program to the design of example heat recovery systems is given and the potential use of the software in other applications described together with proposals for the development of the procedure as a design tool.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
Much of the energy consumed in developed countries is for residential heating and cooling. Substantial savings are possible if one can monitor the indoor environment at many locations, and then actively control the heating, ventilation and air conditioning (HVAC) system. This project uses a wireless sensor array and dedicated microcontroller system to control a residential HVAC system. A low data rate, ad-hoc network of sensors is deployed throughout a residence, with the data sent to a central controller. A graphical user interface allows the resident to monitor the system status, and to set parameters.

This study aims at a preliminary characterization of system operation uncertainty. It bases this on an analysis of the energy consumption of 6 existing buildings on the Georgia Tech campus. The analysis is speculative in nature.

More energy efficient fans, i.e. larger sizes running at lower speeds, in Heating Ventilation and Air Conditioning (HVAC) systems decrease the fan noise and increase the importance of flow generated noise in other system components, e.g., dampers and air terminal devices. In this thesis, an extended prediction model, using semi-empirical scaling laws, for flow noise prediction in HVAC systems at low Mach number flow speeds is presented. The scaling laws can be seen as a combination of a generalized noise spectrum based on experimental data and constriction flow characteristics, where the latter can be gained from ComputationalFluid Dynamics (CFD) simulations. The flow generated noise can be predicted by semi-empirical scaling laws to avoid a time consuming, fully resolved simulation or measurement. Here, an approach is suggested where the general noise spectra are combined with turbulent data obtained from Reynolds Average Navier Stokes (RANS) simulations. A model is proposed using a momentumflux assumption of the dipole source strength and a frequency scaling based on the constriction pressure loss. To evaluate the applicability of the semi-emprical scaling law on different HVAC geometries both literature data and new measurement data are considered. Focus is at comparing geometries of high and low pressure loss but also to discuss the differences in other properties, e.g. radiation characteristics. A general noise reference spectrum is determined bya best fit calculation of measurement data including orifice, damper and bend geometries. Air terminal devices at the end of a duct are also evaluated and compared to constrictions inside ducts. The expected accuracy of the suggested model and its challenges as a tool for flow noise prediction of non-rotating components in HVAC systems are discussed.
På grund av ökade energieffektivitetskrav har större fläktar som roterar med lägre hastighet börjat användas i byggnaders ventilationssystem(HVAC). De lägre hastigheterna har minskat ljudnivån från fläkten och ökat betydelsen av strömningsalstrat ljud från andra systemkomponenter, t.ex. spjäll och luftdon. I denna avhandling presenteras en förbättrad prediktionsmodell, utifrån semi-empiriska skalningslagar, för strömningsalstrat ljud i ventilationssystem. Skalningslagarna kan ses som en kombination av generellaljudspektra och strypningens specifika flödesegenskaper, där det senare kan fås från Computational Fluid Dynamics (CFD) simuleringar. Semiempiriska skalningslagar är ett alternativ för att undvika tidskrävandemätningar eller fullt upplösta simuleringar. Ett tillvägagångssätt presenteras här där det generella spektrat, bestämt utifrån experimentell data, kombineras med data från Reynolds Average Navier Stokes (RANS) simuleringar. En prediktionsmodell föreslås där källstyrkan hos dipolkrafterna definieras utifrån rörelsemängd och frekvensskalningen utifrån strypningens tryckfall. För att utvärdera vilka HVAC geometrier som kan ingå i den generella modellen analyseras både resultat från litteraturen samt nya mätningar. Avhandlingsarbetet fokuserar på att jämföra geometrier av högt och lågt tryckfall men också på att diskutera skillnader i andra egenskaper såsom strålningskarakteristik t.ex. genom att jämföra luftdon i slutet av en kanal med strypningar inuti kanalen. Ett generellt ljudspektrum föreslås utifrån en anpassning av mätdata för strypningar, spjäll och böjar. Modellens förväntade noggrannhet och dess utmaningar som prediktionsverktyg för icke-roterande komponenter i ventilationssystem diskuteras.

QC 20150518

Commercial buildings present a significant potential for energy saving, in particular through the operation of their air-conditioning systems. Regulations and policies are driving to- wards better energy efficiency, but are mainly focused on the design stages. In practice, there is a performance gap between design and operation of new buildings. One of the causes of this gap is the failure to commission buildings for energy efficiency and comfort. Historically commissioning is limited to design compliance and security checks and would need to scale up to account for the necessity of reducing new buildings’ energy foot- print. However, due to inevitable delays in the construction process, commissioning tends to stop when the building reaches ‘practical completion’. It is thus difficult to increase the commissioning scope pre-handover. Additionally, currently available measurements are not suitable to go through the remaining ‘snag’ list or understand comfort and energy issues. This thesis examines the potential and practicality of using wireless sensor networks (WSN) to perform a post occupancy commissioning (PO-Cx) process for energy and occu- pants’ comfort performance. Requirements for such a WSN are determined from analysing building commissioning practices and the faults they fail to identify. An ad hoc WSN prototype is developed and verified. A proof of concept is established through a six-month deployment on a newly refurbished university building. It is concluded that WSN is a key tool to integrate energy performance and comfort to the commissioning of new buildings. The possibility to install them with minimum disruption of an occupied building and to analysis the data remotely means that it is easy to perform additional commissioning tasks post-handover. Recommendations to develop a commercial WSN for PO-Cx application are given and general strategies for PO-Cx are discussed based on the experience acquired.

The energy consumed by heating, ventilating, and air conditioning (HVAC) systems has increased in the past two decades. Thus, improving efficiency of HVAC systems has gained more and more attentions. This concern has posed challenges for modeling and optimizing HVAC systems. The traditional methods, such as analytical and statistical methods, are usually computationally complex and involve assumptions that may not hold in practice since HVAC system is a complex, nonlinear, and dynamic system. Data-mining approach is a novel science aiming at extracting system characteristics, identifying models and recognizing patterns from large-size data set. It has proved its power in modeling complex and nonlinear systems through various effective and successful applications in industrial, business, and medical areas. Classical data-mining techniques, such as neural networks and boosting tree have been largely applied into modeling HVAC systems in literature. Evolutionary computation, including swarm intelligence, have rapidly developed in the past decades and then applied to improving the performance of HVAC systems. This research focuses on modeling, optimizing, and controlling an HVAC system. Data-mining algorithms are first utilized to extract predictive models from experimental data set at Energy Resource Station in Ankeney. Evolutionary algorithms are then employed to solve the optimization models converted from the above data-driven models. In the optimization process, two set points of the HVAC system, supply air duct static pressure set point and supply air temperature set point, are controlled aiming at improving the energy efficiency and maintaining thermal comfort. The methodology presented in this research is applicable to various industrial processes other than HVAC systems.

To improve future Heating Ventilation and AirConditioning (HVAC) systems’ energy efficiency, a reliable wayto determine occupancy is needed. The answer lies within theoccupancy counters. In this project the goal was to study whichsensor would be most suited for detecting occupants, and proposea new application of a combination of occupancy counters andestimator that would be the most suited for determining thenumber of occupancy. The project will cover a literature studyof hardware-based occupancy counters and a software-basedoccupancy estimator, compare their strengths and weaknesses,and several simulations that handle temperature changes andcarbon dioxide (CO2) concentrations. This is to demonstrate andreflect how occupancy affect these levels in an office environment.The results of this work shows that it is possible to improve theenergy efficiency of the HVAC system when using occupancycounters, and the impact of occupancy differ depending on whatis regulated.

Buildings are the primary user of energy in the USA. Within homes, the heating, ventilation, and air condition (HVAC) system is the largest energy consumer. This study: (i) developed a new methodology for simulating filter loading in-situ; (ii) observed the impact of filter loading on AC performance in-situ; and (iii) provided a greater understanding of when a filter is “dirty” and should be replaced. Six central AC systems in the Atlanta metro-region were evaluated. Filter loading was simulated by installing the TrueFlow® airflow metering device and partially taping off the face at 3 different increments. This resulted in measurements at 5 discrete static pressures (no filter, TrueFlow, TrueFlow Taped one, TrueFlow Taped two, and TrueFlow Taped three). The pilot study found that as filter pressure drop increased, airflow rates generally decreased, resulting in higher differences in temperature across the evaporator coil (∆T). There was no observed correlation between absolute humidity across the evaporator coil and either filter pressure drop or system airflow. Overall, as airflow decreased so did sensible, latent, and total capacity. This research can inform decisions about filter replacement and be used to evaluate computer simulation models of HVAC performance.

This thesis presents techniques for improving building HVAC system performance in existing buildings generated using simulation-based tools and real data. Therefore, one of the aims has been to research the needs and possibilities to assess and improve building HVAC system performance. In addition, this thesis aims at an advanced utilization of building energy management system (BEMS) and the provision of useful information to building operators using simulation tools.

Buildings are becoming more complex systems with many elements, while BEMS provide many data about the building systems. There are, however, many faults and issues in building performance, but there are legislative and cost-benefit forces induced by energy savings. Therefore, both BEMS and the computer-based tools have to be utilized more efficiently to improve building performance.

The thesis consists of four main parts that can be read separately. The first part explains the term commissioning and the commissioning tool work principal based on literature reviews. The second part presents practical experiences and issues introduced through the work on this study. The third part deals with the computer-based tools application in design and operation. This part is divided into two chapters. The first deals with improvement in the design, and the second deals with the improvement in the control strategies. The last part of the thesis gives several rules for fault diagnosis developed using simulation tools. In addition, this part aims at the practical explanation of the faults in the building HVAC systems.

The practical background for the thesis was obtained though two surveys. The first survey was carried out with the aim to find the commissioning targets in Norwegian building facilities. In that way, an overview of the most typical buildings, HVAC equipment, and their related problems was obtained. An on-site survey was carried out on an example building, which was beneficial for introducing the building maintenance structure and the real hydronic heating system faults.

Coupled simulation and optimization programs (EnergyPlus and GenOpt) were utilized for improving the building performances. These tools were used for improving the design and the control strategies in the HVAC systems. Buildings with a hydronic heating system were analyzed for the purpose of improving the design. Since there are issues in using the optimization tool, GenOpt, a few procedures for different practical problems have been suggested. The optimization results show that the choice of the optimization functions influences significantly the design parameters for the hydronic heating system.

Since building construction and equipment characteristics are changing over time, there is a need to find new control strategies which can meet the actual building demand. This problem has been also elaborated on by using EnergyPlus and GenOpt. The control strategies in two different HVAC systems were analyzed, including the hydronic heating system and the ventilation system with the recovery wheel. The developed approach for the strategy optimization includes: involving the optimization variables and the objective function and developing information flow for handling the optimization process.

The real data obtained from BEMS and the additional measurements have been utilized to explain faults in the hydronic heating system. To couple real data and the simple heat balance model, the procedure for the model calibration by use of an optimization algorithm has been developed. Using this model, three operating faults in the hydronic heating system have been elaborated.

Using the simulation tools EnergyPlus and TRNSYS, several fault detection and diagnosis (FDD) rules have been generated. The FDD rules were established in three steps: testing different faults, calculating the performance indices (PI), and classifying the observed PIs. These rules have been established for the air cooling system and the hydronic heating system. The rules can diagnose the control and the component faults. Finally, analyzing the causes and the effects of the tested faults, useful information for the building maintenance has been descriptively explained.

The most important conclusions are related to a practical connection of the real data and simulation-based tools. For a complete understanding of system faults, it is necessary to provide real-life information. Even though BEMS provides many building data, it was proven that BEMS is not completely utilized. Therefore, the control strategies can always be improved and tuned to the actual building demands using the simulation and optimization tools. It was proven that many different FDD rules for HVAC systems can be generated using the simulation tools. Therefore, these FDD rules can be used as manual instructions for the building operators or as a framework for the automated FDD algorithms.

Denne avhandlingen presenterer noen fremgangsmåter for forbedring av ytelser for VVS-tekniske anlegg i eksisterende bygninger basert på bruk av simuleringsverktøy og virkelige måledata. Ett av målene har vært å undersøke behov og muligheter for vurdering og forbedring av ytelser for VVS-anlegg i bygninger. I tillegg har denne avhandlingen hatt som mål å fremme bruk av SD-anlegg samt å fremskaffe nyttig informasjon til driftspersonalet.

Bygninger blir stadig mer kompliserte systemer som inneholder flere og flere komponenter mens SD-anlegg håndterer en stadig større mengde data fra bygningsinstallasjoner. På den ene siden registreres det ofte feil og problemer med hensyn til ytelsene til de VVS-tekniske installasjonene. På den andre siden innføres det stadig strengere lovmessige pålegg og kost-nyttekrav motivert i energieffektiviseringen. SD-anlegg og databaserte verktøy bør derfor brukes mer effektivt for forbedring av ytelsene.

Avhandlingen består av fire hoveddeler hvor hver del kan leses separat. Den første delen, som er basert på literatturstudie, forklarer funksjonskontroll som begrep og prinsipper for oppbygging av verktøy for funksjonskontroll. Den andre delen presenterer praktisk erfaring og problemstillinger utviklet og behandlet i løpet av arbeidet med avhandlingen. Den tredje delen handler om anvendelse av databaserte verktøy for forbedring av ytelsen for VVS-tekniske installasjoner. Den tredje delen er delt opp i to kapitler, hvorav et handler om forbedring av systemløsninger og et om forbedring av styringsstrategier. Den siste delen presenterer flere regler for feilsøking og diagnostisering utviklet gjennom bruk av simuleringsverktøy. I tillegg gir denne delen en praktisk forklaring av feilene i de VVS-anleggene som er behandlet i undersøkelsen.

Det praktiske grunnlaget for avhandlingen er etablert gjennom to undersøkelser. Den første var en spørreundersøkelse som hadde til hensikt å kartlegge målsetninger for funksjonskontroll i norske bygninger. Gjennom dette ble det etablert en oversikt over de mest typiske bygninger med tilhørende VVS-anlegg og de mest forekommende problemene. En dypere undersøkelse ble utført på ett casebygg. Denne undersøkelsen viste seg å være nyttig både for kartlegging av betydningen av organisering av driften av bygningen og for avdekking av de virkelige feilene i det vannbårne oppvarmingssystemet.

En kobling mellom et simulerings- og et optimaliseringsprogram (EnergyPlus og GenOpt) har vært benyttet for forbedring av ytelsene for de VVS-tekniske installasjonene. Disse verktøyene har vært brukt for forbedring av både systemløsningene og styringsstrategiene for VVS-anlegg. Bygninger med vannbåren oppvarmingssystem har vært analysert for å forbedre systemløsningen. På grunn av begrensninger i bruken av optimaliseringsverktøyet GenOpt, har det blitt utviklet egne prosedyrer for håndtering av visse typer problemstillinger hvor denne begrensningen opptrer. Resultatene for optimaliseringen viser at valg av objektfunksjoner påvirker betydelig parametrene i det vannbårne oppvarmingssystemet.

Endringer i egenskapene til både bygningskonstruksjoner og utstyr som skjer på grunn av aldring over tiden, gjør det nødvendig med tilpassning av styringsstrategier slik at det virkelige behovet kan bli dekket. Denne problemstillingen har vært analysert ved bruk av EnergyPlus og GenOpt. Styringsstrategiene for to forskjellige VVS-anlegg, et vannbåret oppvarmingssystem og et ventilasjonsanlegg med varmegjenvinner har blitt behandlet. Den utviklete prosedyren for optimalisering av styringsstrategien består av følgende steg: innføring av optimaliseringsvariabler og objektfunksjon, samt utvikling av informasjonsflyt for behandling av optimaliseringsprosessen.

De virkelige data, både fra SD-anlegg og tilleggsmålinger, har vært benyttet for praktisk forklaring av feilene i oppvarmingssystemet. En prosedyre for modellkalibrering basert på bruk av en optimaliseringsalgoritme som kobler sammen de virkelige data og en enkel varmebalansemodell har blitt foreslått. Tre konkrete driftsfeil i oppvarmingssystemet har blitt belyst gjennom bruk av denne varmebalansemodellen.

Flere regler for feilsøking og diagnostisering har blitt utviklet ved hjelp av simuleringsverktøyene EnergyPlus and TRNSYS. Denne utviklingen har bestått av tre ulike steg: testing av bestemte feil, beregning av ytelsesindikatorer og til slutt klassifisering av de observerte ytelsesindikatorer. Reglene har blitt utviklet for et system av aggregater for luftkjøling og for et vannbåret oppvarmingssystem. Reglene kan diagnostisere både styringsfeil og komponentfeil. Til slutt presenteres informasjon som er nyttig for drift av VVS-tekniske installasjoner i bygninger basert på en analyse av årsakene for og virkningene av de feil som er behandlet.

De viktigste konklusjonene er knyttet til praktisk kombinasjon av virkelige måleverdier og simuleringsverktøy. Informasjon fra det virkelig liv er helt nødvendig for å få en god forståelse av feil som oppstår i anlegg. Det er også vist at potensialet som ligger i alle de data som er tilgjengelige gjennom SD-anlegg, ikke er fullt utnyttet. Gjennom bruk av simuleringsverktøy kan styringsstrategiene alltid bli bedre tilpasset og innjustert til de virkelige behov i bygningen. Simuleringsverktøy kan også brukes for utvikling av prosedyrer for feilsøking og diagnostisering i VVS-tekniske anlegg. Disse prosedyrene kan brukes enten som en veileder for manuell feilsøking og detektering eller som grunnlag for utvikling av automatiserte algoritmer.

Paper II, VI and VII are reprinted with kind permission from Elsevier, sciencedirect.com

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University.

Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building's HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (T_s), supply duct static pressure (P_s), chilled water supply temperature (T_w), minimum outdoor ventilation, reheat (or zone supply air temperature, T_z), and chilled water differential pressure set-point (D_pw) are optimized with respect to energy use of the HVAC's cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE's new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August).

This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program's algorithms analyze multiple variables (humidity, pressure, temperature, CO₂, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function's objective, which is the lowest energy consumption while maintaining occupancy comfort.

The efficient use of energy is a major issue nowadays. Environmental and economic purposes push various investigations to focus on the performances of energy systems and equipment. In the context of the coming energy transition, Heat, Ventilation and Air Conditioning (HVAC) systems will certainly take an increasing and worldwide importance. In this work, energy and exergy analysis are used to assess the performances of each component of an air treatment station. Results of energy and exergy analysis for each process are presented. The most important result is that simple heating and cooling processes with deshumidification have the worst exergy efficiencies; and that both processes represent almost all the exergy losses of the studied HVAC system.
Tesis

Heating, Ventilation, and Air-Conditioning (HVAC) systems constitute a signicant portion of the total energy consumed in households. Changing the operation of the HVAC system can thus have signicant impact on the energy savings that a household can achieve. One way of performing this control is using a model predictive approach, where models of the system are formed, and using these models and their future predictions, an optimal control strategy is found. This work is then concerned with evaluating di erent models that can predict room temperature changes in a house while the spatial heating system is on and o , as well as models that can predict the energy consumption associated with the heating system usage. The methodology is an improvement over traditional model predictive control, as the models continuously learn over time, improving their results. Data is obtained from sensors placed in 6 NZEBs in Soesterberg, The Netherlands. Black-box models are formed for each house using linear regression, polynomial regression, and a neural network. The models are updated with new information every week so they are able to learn from new data, and are then evaluated based on the magnitude and behavior of their respective errors. Finally the best model for room temperature predictions is found to be a weighted average of the results from the polynomial regression and neural network. A simple linear ordinary least squares model is used for the prediction of energy consumption. The problem is then formulated as a Markov Decision Process, which allows the system to reduce energy consumption while maintaining user comfort. The genetic algorithm is used to find an optimal control strategy. An optimal control strategy is found with a 24 hour look ahead, while the models take into consideration current weather conditions (also available in the future through weather forecasts) and previous room temperatures. One house was nally taken as an example, where its models were used and an optimal control strategy (a series of set point temperatures) was found for the spatial heating system for every hour over one week in December. The results showed a signicant decrease in energy consumption. The methods used in this work make the loads much more predictable,  and allow the exibility o ered by the spatial heating system and the thermal mass of the house to be taken advantage o . The houses at hand are NZEBs and are well designed with small losses, further increasing the potential for energy savings.

The energy consumption of the heating and cooling coils of the air handling units of an educational building with multiple laboratories has been evaluated and suitable energy recovery methods were assessed in an attempt to reuse the energy lost in the exhaust air stream and promote energy conservation while complying with the current building codes. HVAC system of Life Sciences II (LS II) building is a constant air volume (CAV), 100% outside air (OA) system. Hourly surface temperature data for southern Illinois was used to determine energy consumption from current system and glycol run-around loop heat recovery system was designed for air handling unit (AHU) 1 and annual energy savings of 17.8% were estimated with a payback period of 5.1 years. Return air heat recovery system was designed for AHU 3. Annual energy savings estimated for AHU 3 are 17.89% with a payback period of 6 months. Both Glycol run around loop heat recovery system and return air system exhibit significant energy saving potential with a quick payback, making them good choices for retrofitting the educational facility.

Researchers and practitioners have found that the type of mechanical system utilized to thermally condition a space impacts the noise level for occupants. Indeed, in schools, air conditioning systems are by far the largest contributors to room noise (Bradley, 2002; Nelson et al., 2005; Siebein et al., 2000). Studies have also demonstrated the impact of noise on youth\'s cognitive performance. The problem is worsened in non-native speakers and children with hearing loss (which can be temporary due to colds and allergies or permanent). No studies yet have bridged those two widely-supported findings: if the type of mechanical system impacts (and often dictates) the noise level in the room, and if the noise level in the room impacts the performance of the student, might there be a correlation between mechanical system type and student achievement? An examination of 73 elementary schools in a single Orlando, Florida school district suggests that, for schools populated with students of similar socio-economic background, schools cooling with the noisiest types of mechanical system, with both a compressor and fan exposed to the room, underperformed on standardized student achievement tests relative to those with quieter types of systems. Also, schools with the highest percentages of low socio-economic level children are more likely to get the noisiest type of cooling system. Mechanical system data was gathered through an online survey answered by facility maintenance managers and school percentage student achievement scores on the Florida Comprehensive Assessment Test (FCAT) were obtained from public online data for years 2003 to 2010 for third grade only. This is the earliest students are tested by the FCATs and studies show a larger impact of noise at an early age. This study examined as well the extent to which teachers believe noise from mechanical systems has an effect on student learning and under what conditions. Results from an online survey sent to third grade teachers in the same schools show that teachers generally judge noise levels in their classroom to be sufficiently quiet and do not consider noise to be a problem that needs addressing. However, in open-ended questions teachers demonstrated an understanding of the effects of noise in children\'s concentration and classroom speech communication.
Ph. D.

This study addresses 5 uncertainties that exist in the operation of HVAC systems, which will presumably affect the actual energy consumption of the HVAC system in comparison to the consumption under idealized bahavior. We consequently add these parameters and their uncertainty range into the source code, eventually resulting in an EnergyPlus program in which the HVAC operation uncertainty is embedded as so-called model form uncertainty. The upgraded EnergyPlus is tested for each parameter uncertainty separately, and to show the impact of each uncertainty albeit for hypothetical uncertainty ranges of the parameters.

Commissioning of HVAC systems has potential for significant improvements in occupant satisfaction, comfort and energy consumption, but is very labour-intensive and expensive as practiced at this time. Previous investigators have capitalized on digital control systems' capability of logging and storing data and of interfacing with external computers for open loop control by developing methods of automated fault detection and diagnosis during normal operation. Some investigators have also considered the application of this technique in commissioning. This thesis investigates the possibility of utilizing first principles and empirical models of air-handling unit components to represent correct operation of the unit during commissioning. The models have parameters whose values can be determined from engineering design intent information contained in the construction drawings and other data available at commissioning time. Quasi-dynamic models are developed and tested. The models are tested against design intent information and also against data from a real system operating without known faults. The results show the models agree well with the measured data except for some false positive indications, particularly in the damper and fan models, during transients. A procedure for estimating uncertainty in the instrumentation and the models is developed. The models are also tested against artificial faults and are able to detect all of the faults. Methods of diagnosing the faults are discussed.

Heating, ventilating and air-conditioning (HVAC) system is complex non-linear system with multi-variables simultaneously contributing to the system process. It poses challenges for both system modeling and performance optimization. Traditional modeling methods based on statistical or mathematical functions limit the characteristics of system operation and management. Data-driven models have shown powerful strength in non-linear system modeling and complex pattern recognition. Sufficient successful applications of data mining have proved its capability in extracting models accurately describing the relation of inner system. The heuristic techniques such as neural networks, support vector machine, and boosting tree have largely expanded to the modeling process of HVAC system. Evolutionary computation has rapidly merged to the center stage of solving the multi-objective optimization problem. Inspired from the biology behavior, it has shown the tremendous power in finding the optimal solution of complex problem. Different applications of evolutionary computation can be found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply the evolutionary computation approach in optimizing the performance of HVAC system. The energy saving can be achieved by implementing the optimal control setpoints with IAQ maintained at an acceptable level. A trade-off between energy saving and indoor air quality maintenance is also investigated by assigning different weights to the corresponding objective function. The major contribution of this research is to provide the optimal settings for the existing system to improve its efficiency and different preference-based operation methods to optimally utilize the resources.

As modern life develops, humans spend most of their time inside buildings. Understanding the effects of different building materials that exist indoors on indoor air quality is crucial to ensure comfort, health, and productivity of building occupants. Indoor air quality (IAQ) is an important field of building science that focuses on studying the existence of different compounds indoors. These compounds include: airborne particles such as dust, volatile organic compounds (VOCs) such as carbonyls, reactive gases such as radon, ozone and others. Ozone is a strong oxidant gas that has adverse effects on human health, and is highly reactive with building materials that exist indoors. This reaction may reduce its concentration indoors, but may produce other by-products that could be more harmful for human health than ozone itself. In this dissertation, ozone reaction with different building materials is investigated in four studies. The first includes studying the effect of indoor carpet fiber type on ozone removal and carbonyl emissions. This study provides valuable data and knowledge about the importance of selecting carpet type and its effect on indoor environment. In the second study, different indoor plants were tested to evaluate their ability to remove ozone. The results from this study show wide variation between plants tested on ozone removal. Also, the ability of plants as ozone removal agent changes as light levels change. The third part studies ozone removal efficiency of HVAC filters previously installed in air handling units located on green and white membrane roofs of a commercial building. Detailed filter surface analysis using scanning electron microscope (SEM) was performed to understand the nature of deposits on these filters. The reason for differences in ozone removal efficiency of two filters in comparison with new filter is also discussed. The fourth study investigated ozone removal and carbonyl emissions from three different VOC content indoor latex paints. The outcomes from this research show that zero VOC latex paint has the most ozone effective removal capacity and this paint is the least carbonyl emitter. The research presented in this dissertation adds new data, valuable knowledge, and expands the understanding of the importance of selecting indoor materials to raise indoor air quality and make the buildings' indoor environment healthier and safer.

The HVAC system configuration is a conceptual design of the HVAC system, including the employed components, the topology of the airflow network, and the control strategy with set points. Selection of HVAC system configuration is normally done in the early stage of the design process. The configuration design, however, has significant impacts on the performance of the final system. This thesis describes the development of the design synthesis of optimal HVAC system configurations by Evolutionary Algorithm. In this research, the HVAC system configuration design synthesis has been formulated as an optimisation problem, in which, the component set of the configuration, the topology of the airflow network, and the control set points for the assumed supervisory control strategy, are the optimisation variables. Psychrometrics-based configuration model has been developed in order to evaluate the optimisation objective of minimising the annual energy consumption of the HVAC system. The optimisation is also subjected to a number of design constraints, including the connectivity of the topology, the performance limitations of the components, and the design requirements for the air-conditioned zones. The configuration synthesis problem is a multi-level optimisation problem. The topology depends on the set of selected components, whereas the search space of the control set points changes with the different components and topology. On the other hand, the performance of the configuration is assessed with its optimum operation; therefore the control set points have to be optimised for each configuration solution, before the optimum configuration can be identified. In this research, a simultaneous evolutionary approach has been developed. All optimisation variables of the configuration have been enwded into an integrated genotypic data structure. Evolutionary operators have also been developed to search the topological space (for the optimum topology) and parametric space (for the optimal control set points) at the same time. The performance of the developed approach has been validated with example optimisation problems. It is concluded that the implemented evolutionary algorithm has been able to find (near) optimum solutions for various design problems, though multiple trials may be required. The limitations of this approach and the direction of future development have been discussed.

The issue about how Scania shall perform HVAC tests in the climatic wind tunnel CD7, which Scania is about to complete by year 2013, has resulted in four test methods for truck HVAC and also a test method structure. The test methods have been chosen to focus on sun, snow and rain simulations. These are adapted and developed according to what should be tested and to what can be tested in CD7, which has been the object. With CD7 different climates can be simulated in a controlled environment for complete truck level, from desert with high sun load to arctic cold and snow. The test methods represents a basis to start from, for designers and test engineers which make the testing more repetitive as well as time eventually can be saved. Before the test methods can be applied they must be verified as they are based on theoretical and empirical assumptions. Test methods are not static and must be updated and developed continuously. By having a clear documentation with motivated choices for the methods, changes can easily be made. Another important part is for knowledge not to be lost over time. The aim with the master thesis has been that the study shall be basis for future testmethod development for HVAC at Scania. The structure makes an important component for documentation and the overview perspective. The structure is also flexible enough to be used for other test facilities, not only CD7, which should be a natural continuation. From the literature study in combination with interviews and observations, both internal and external, a modeling has emerged. Developed test methods have been discussed in a workshop with Scania personnel that gave feed-back to the methods. Performed interviews have demonstrated that explorative methods are usually made. At the same time the explorative testing evolve from established internal methods and requirement specifications. Requirement specification and testing are two subjects who have shown to be connected to each other. The interviews have also shown that solar simulations are among the most common tests concerning climate and HVAC.

This thesis develops and experimentally evaluates a sequential model-based detector for detecting actuator failures in HVAC systems. Specifically, this thesis considers actuator failures which result in the actuator valve sticks in an unknown (but constant) position. A first order heat-equation model is assumed to model interactions between adjacent rooms, which is used to formulate a hypothesis testing problem assuming known inter-room thermal parameters. The detector is formulated to provide performance that asymptotically bounds both the probability of miss and the probability of false alarm. A wireless sensor network was deployed and employed in coordination with the campus SCADA system to both demonstrate the detector performance and identify model parameters. Multiple testing scenarios are considered where the inter-room dynamics are physically altered (by opening and closing windows and doors). The time required to accurately determine the inter-room thermal parameters is studied, where parameter identification is performed using a time-series least-squares approach. Results indicate that when the model parameters are accurately known, the detector performance is acceptable; however, inaccurate modeling assumptions lead to significant detector performance degradation.

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.
L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupants

Buildings account for 40% of total energy consumption in the UK and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand and the ascending trend in utilising HVAC&R systems together with the global need to impose energy-efficiency measures underline the importance of selecting the most appropriate HVAC&R system in a design process. In the early stages of the design and construction of a building, the design engineer is responsible for considering various systems in the process of HVAC&R systems selection. Although a broad range of simulation tools is developed for performance evaluation of HVAC&R systems, none of them is capable of performing a decision making process for HVAC&R systems selection. Therefore, the contribution of this study to knowledge has been the development of a multiple attribute decision making tool for HVAC&R systems selection for office buildings in the UK. Firstly, a set of reference office buildings was developed as representative of the UK office building stock and one of them was selected for further study. Then, a set of common alternative HVAC&R systems was identified. The reference office building, assumed to be located in London, together with the alternative HVAC&R systems were simulated in the TRNSVS and their technical performance, economic aspects and environmental impacts were assessed. Finally, to choose the most appropriate system among the alternatives a fuzzy multiple attribute decision making method was used to formulate the process of decision making. The scope of this study was further extended by considering 18 climate regions in the UK together with the effect of climate change in the decision making process using the degree-days theory. In addition, the UK Government's electricity decarbonisation plans were integrated to the developed decision making model. Finally, the model was transferred into a computational tool with a user-friendly interface developed in Matlab.

During the last 10 years solar cooling systems attracted more and more interest not only in the research area but also on a private and commercial level. Several demonstration plants have been installed in different European countries and first companies started to commercialise also small scale absorption cooling machines. However, not all of the installed systems operate efficiently and some are, from the primary energy point of view, even worse than conventional systems with a compression chiller. The main reason for this is a poor system design combined with suboptimal control. Often several non optimised components, each separately controlled, are put together to form a ‘cooling system’. To overcome these drawbacks several attempts are made within IEA task 38 (International Energy Agency Solar Heating and Cooling Programme) to improve the system design through optimised design guidelines which are supported by simulation based design tools. Furthermore, guidelines for an optimised control of different systems are developed. In parallel several companies like the SolarNext AG in Rimsting, Germany started the development of solar cooling kits with optimised components and optimised system controllers. To support this process the following contributions are made within the present work: - For the design and dimensioning of solar driven absorption cooling systems a detailed and structured simulation based analysis highlights the main influencing factors on the required solar system size to reach a defined solar fraction on the overall heating energy demand of the chiller. These results offer useful guidelines for an energy and cost efficient system design. - Detailed system simulations of an installed solar cooling system focus on the influence of the system configuration, control strategy and system component control on the overall primary energy efficiency. From the results found a detailed set of clear recommendations for highly energy efficient system configurations and control of solar driven absorption cooling systems is provided. - For optimised control of open desiccant evaporative cooling systems (DEC) an innovative model based system controller is developed and presented. This controller consists of an electricity optimised sequence controller which is assisted by a primary energy optimisation tool. The optimisation tool is based on simplified simulation models and is intended to be operated as an online tool which evaluates continuously the optimum operation mode of the DEC system to ensure high primary energy efficiency of the system. Tests of the controller in the simulation environment showed that compared to a system with energy optimised standard control the innovative model based system controller can further improve the primary energy efficiency by 19 %.

Faults that develop in the heat exchanger subsystems in air-conditioning installations can lead to increased energy costs and jeopardise thermal comfort. The sensor and control signals associated with these systems contain potentially valuable information about the condition of the system, and energy management and control systems are able to monitor and store these signals. In practice, the only checks made are to verify set-points are being maintained and that certain critical variables remain within predetermined limits. This approach may allow the detection of certain abrupt or catastrophic faults, but degradation faults often remain undetected until their effects become quite severe. This thesis investigates the appropriateness of using mathematical models to track the development of degradation faults. An approach is developed, which is based on the use of analytical models in conjunction with a recursive parameter estimation algorithm. A subset of the parameters of the models, which are closely related to faults, is estimated recursively. Significant deviations in the values of the estimated parameters from nominal values, which represent `correct operation', are used as an indication that the system has developed a fault. The extent of the deviation from the nominal values is used as an estimate of the degree of fault. This thesis develops the theory and examines the robustness of the parameter estimator using simulation-based testing. Results are also presented from testing the fault detection and diagnosis scheme with data obtained from a simulated air-conditioning system and from a full size test installation.

Nowadays, the building sector is a substantial consumer of world’s energy. The dominant energy share of Heating, Ventilation and Air-Conditioning (HVAC) systems, makes it the focus of research for saving energy. Current air conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain sufficient comfort level. Having information regarding occupancy situation may lead to significant energy-savings. On the other hand, focusing on the reduction of energy only, may lead to sacrificing the thermal comfort of the occupants in a building. Moreover, due to the difference of preference of thermal comfort of individuals, particularly in a shared space, a fixed set point for HVAC systems, can cause discomfort. Therefore, a comprehensive technique is required to save energy while maintaining thermal comfort. The present research proposes an occupancy-driven HVAC control system based on thermal comfort analysis. A ZigBee-based indoor localization system is developed to monitor the location of occupants inside the buildings. Algorithms are used to improve the accuracy of positioning system, which include Near Neighbour Area (NNA), Principle Component Analysis (PCA) and Exponential Moving Average algorithms (EMA). Computational Fluid Dynamics (CFD) is used to simulate the thermal comfort through modelling the indoor air distribution and flows. Wind velocity and temperature are simulated in several scenarios and the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) are computed. The simulation results are verified through a survey asking for occupants’ real feelings and consequently thermal comfort zones are identified with associated occupants, which are used for possible energy saving while providing satisfied level to all the occupants. To investigate different satisfaction feeling of occupants, a personalized thermal profile is created for individuals inside the test bed area. A fuzzy based approach is used to develop a fuzzy map of each occupant and as a result, a personal thermal preference profile is created. Based on the present occupants in the room, the minimum and maximum preferred temperatures are estimated and used for controlling the HVAC system. The Semi-hidden Markov chain method is used to create the occupants’ behavioural pattern which can reduce the frequencies of turning ON or OFF the HVAC systems. The real-time locations of the persons, estimated based on the NNA and MA localization method, are combined with their behavioural patterns and thermal preference profiles and their comfort zones to control the corresponding HVACs. The proposed method has been implemented to a shared office occupied by nine users and equipped with two individual air conditioners. The comparison of different control strategies show that the proposed intelligent control has a significant potential of saving energy and at the same time maintaining occupants in a reasonable thermal comfort range.

Thesis: S.M. in Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2015.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 74-77).
Secondary frequency regulation is a necessary electric grid ancillary service that balances electric power system supply and demand on short time intervals of seconds to minutes. Commercial HVAC chillers may be well positioned to provide secondary frequency regulation as a demand side resource. Commercial 200 ton (703 kWth) chillers serving two buildings in the Boston area are used to experimentally develop a practical closed-loop controller that modifies chiller power demand to provide secondary frequency regulation. In the first setup, a physical controller is connected directly to the chiller and adjusts power through chilled water setpoint. In the second setup, both the chiller and air handling units are controlled through the BAS. Demonstrations using standard electric system operator test routines show the chiller power response to exceed qualification requirements while providing up to +/-25% of chiller nameplate power in secondary frequency regulation capability. The controller is further demonstrated to provide secondary frequency regulation continuously for several hours longer than the standard test routines, during which building cooling load changes significantly. Analysis of results indicate minimum power and variable COP as two factors that could be incorporated into future models to more accurately reflect observed chiller transient behaviour and predict performance. BAS communication delays, ramp rate limits, and compressor cycling are additional factors that can have significant negative impacts on controller performance. Extrapolation of experiment results to higher-level analysis indicates that chillers can contribute to the secondary frequency regulation requirements at the grid level in aggregate, although potential varies greatly depending on climate and building type. There is more potential in the south, where 21% of secondary frequency regulation requirements might be met with chillers; the contribution of chillers in colder climates is minimal. Short-term power balance to achieve stability is essential for the operation of the modern electrical power system. Providing stability through modified control of existing HVAC chillers in commercial buildings is a technologically feasible alternative to existing solutions and can make a meaningful contribution to the electrical grid.
by Po-An (Leo) Su.
S.M. in Building Technology

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 135-138).
The building sector of the United States currently consumes over 41% of the United States primary energy supply. Estimates suggest that between 5 and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate those building system faults that cause large building energy inefficiencies. The technical contributions of this work include expert rules that adapt to HVAC equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. These contributions are particularly unique in their treatment of model and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault detection, first order faults such as simultaneous heating and cooling and imbalanced airflows within several large air-handling units were targeted. Savings of around $22,500 were predicted when the fault of simultaneous heating and cooling was occurring over the entire month in an air handler. Some of the other faults include the heat recovery pump operation in air handling units. An example of the potential energy savings in a large hospital that has been monitored is presented. Yearly savings of around $24,000 were predicted by correcting the operation of the heat recovery pump. Faults in the cooling system were also explored. The paper presents a strategy to load multiple centrifugal chillers with a monotonically increasing COP vs. partload ratio curve. The paper also considers the optimization of cooling tower fan operation coupled with the chiller operation. Models for cooling tower performance and chiller performance are used to define the operation that yields the minimum energy consumption of the system. The number of cooling tower fans and the use of multiple towers with a single chiller are considered. The optimum operation is compared to actual operations to signal operating faults. User testing and experiments show that embracing uncertainty within HVAC fault detection and evaluation is not only paramount to judicious fault inference but it is also central to gaining the trust and buy-in of system users who ultimately can apply fault detection information to actually fix and improve building operations.
by Suhrid Avinash Deshmukh.
S.M.