Relevant bibliographies by topics / Heating load

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heating load'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Heating load"

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Wang, Chao, Chun Hua Sun, Cheng Ying Qi, Feng Yun Jin, and Bo Shao. "The Experimental Analysis on the Regularity of Heating Load in Buildings Using Heat Meters with On-Off Time and Area Method in Shijiazhuang Area." Applied Mechanics and Materials 694 (November 2014): 272–75. http://dx.doi.org/10.4028/www.scientific.net/amm.694.272.

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Based on the heating load experiment adopting on-off time and area measuring method of typical heat users located in different places of different buildings in Shijiazhuang, the heating loads of the heat users which are summarized as two typical heat using modes are studied in this paper and the heating loads in the whole winter heating season of the building is estimated. The results shows that with the user’s heat adopting ratio in these two modes increases, the corresponding heating load is lower, the heating load distribution curve gets smoothly in the whole heating period and the peak value of the system heating loads reduces. This regularity of heating load distribution is beneficial for enhancing the stability of heating system and the thermal efficiency of the heat source.
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Bi, Yuehong, Lingen Chen, and Fengrui Sun. "Heating load, heating-load density and COP optimizations of an endoreversible air heat-pump." Applied Energy 85, no. 7 (July 2008): 607–17. http://dx.doi.org/10.1016/j.apenergy.2007.09.007.

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Houšová, J., and K. Hoke. "Microwave heating – the influence of oven and load parameters on the power absorbed in the heated load." Czech Journal of Food Sciences 20, No. 3 (November 18, 2011): 117–24. http://dx.doi.org/10.17221/3521-cjfs.

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The microwave power is a parameter greatly influencing the rate of heating. Several authors reported on certain differences between the rated power output as a parameter used by oven manufacturers in the labelling of ovens, and the power actually delivered to the heated product. A review of the respective information is given in this article together with the results of own experiments following the influence of the oven type and the heated substance parameters on the power actually absorbed in the substance volume during its heating. As the heated substance, water and solutions of NaCl and sucrose of different concentrations were used. For the heating, four types of domestic microwave ovens and glass and plastic containers, were used. The decreasing of the efficiency of heating with the decreasing volume of the heated substance and a certain relation between the rate of this decrease and the types of oven and of substance was estimated. With the small cavity ovens, a lower rate of the decrease of the absorbed microwave power with the decreasing volume of the substance was found as compared to the large cavity oven. A certain influence of other technical oven parameters is shown in the comparison of the tests results with the ovens of the same rated power and the cavity volume. In addition to the substance volume, also its dielectric properties probably influence the microwave power absorbed in small samples during the heating. No simple dependence can be seen on the basis of the tests results between the type of container used in the tests and the power absorbed in the heated substance.  
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Hajabdollahi, Hassan, and Zahra Hajabdollahi. "Economic feasibility of trigeneration plants for various prime movers and triple load demands." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 3 (July 31, 2015): 371–82. http://dx.doi.org/10.1177/0954408915597832.

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In this paper, after thermal and economic modeling of cogeneration plant, this system is optimized to find the optimal prime mover and their benefit for various cooling, heating, and electrical demand loads. To find the optimal prime mover and their benefit for each triple load, two new nondimensional design parameters including electric cooling ratio and nominal power ratio are defined. It is observed that, for example, for higher electrical and lower heating load demands, the gas engine is more profitable while for higher electrical and heating load demands, diesel engine is more profitable. In addition, some ranges of demand loads at which using CCHP plant is not profitable (in comparison with traditional system) are also obtained and presented. The optimum results obtained in NO SELL mode show that the highest values of actual annual benefit (AAB) are obtained for highest values of electrical load demand. This region corresponds with values of Hdmn/ Qdmn (heating to cooling load demand ratio) in the range of 1.5–3.5. The highest values of AAB for SELL mode are obtained to be in the range of 0.5–3.5 for Hdmn/ Qdmn (heating to cooling load demand ratio).
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Yang, Yunbo, Rongling Li, and Tao Huang. "Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting." Energies 13, no. 17 (August 21, 2020): 4343. http://dx.doi.org/10.3390/en13174343.

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In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.
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Chen, S. S., N. T. Wright, and J. D. Humphrey. "Heat-Induced Changes in the Mechanics of a Collagenous Tissue: Isothermal, Isotonic Shrinkage." Journal of Biomechanical Engineering 120, no. 3 (June 1, 1998): 382–88. http://dx.doi.org/10.1115/1.2798005.

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We present data from isothermal, isotonic-shrinkage tests wherein bovine chordae tendineae were subjected to well-defined constant temperatures (from 65 to 90°C), durations of heating (from 180 to 3600 s), and isotonic uniaxial stresses during heating (from 100 to 650 kPa). Tissue response during heating and “recovery” at 37°C following heating was evaluated in terms of the axial shrinkage, a gross indicator of underlying heat-induced denaturation. There were three key findings. First, scaling the heating time via temperature and load-dependent characteristic times for the denaturation process collapsed all shrinkage data to a single curve, and thereby revealed a time-temperature-load equivalency. Second, the characteristic times exhibited an Arrhenius-type behavior with temperature wherein the slopes were nearly independent of applied load—this suggested that applied loads during heating affect the activation entropy, not energy. Third, all specimens exhibited a time-dependent, partial recovery when returned to 37°C following heating, but the degree of recovery decreased with increases in the load imposed during heating. These new findings on heat-induced changes in tissue behavior will aid in the design of improved clinical heating protocols and provide guidance for the requisite constitutive formulations.
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Fox, B., A. I. McCartney, and B. M. McCann. "Scheduling of radio-controlled heating load." IEE Proceedings - Generation, Transmission and Distribution 145, no. 6 (1998): 641. http://dx.doi.org/10.1049/ip-gtd:19982356.

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Van Der Meulen, S. F. "Load management in district heating systems." Energy and Buildings 12, no. 3 (November 1988): 179–89. http://dx.doi.org/10.1016/0378-7788(88)90063-1.

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Li, Hongwei, and Stephen Jia Wang. "Load Management in District Heating Operation." Energy Procedia 75 (August 2015): 1202–7. http://dx.doi.org/10.1016/j.egypro.2015.07.155.

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Lu, Yu, and Rui Li. "Analysis of Influencing Factors of Heat Load of Heating Power Station." E3S Web of Conferences 252 (2021): 01042. http://dx.doi.org/10.1051/e3sconf/202125201042.

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Heating system load forecasting is very important in heating system planning and formulating heating plans. More accurate load forecasting can obtain greater economic and social benefits for the heating power station. The selection of influencing factors affecting heating load forecasting has a decisive effect on the accuracy of load forecasting. Based on the analysis of heat load of typical heating power station, this paper compares and analyses the influence of outdoor temperature, wind speed and load of previous days, etc. The main factors that should be considered in heating load forecasting are obtained.
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Dissertations / Theses on the topic "Heating load"

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Jerome, David. "Building load analysis and graphical display as a design tool." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/16410.

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Cleaveland, John P. "Loadcal : a microcomputer simulation for estimating heating and cooling loads for commercial buildings." Thesis, Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/23099.

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Rodriguez, German Darío Rivas. "Decentralized Architecture for Load Balancing in District Heating Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3329.

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Context. In forthcoming years, sustainability will lead the development of society. Implementation of innovative systems to make the world more sustainable is becoming one of the key points for science. Load balancing strategies aim to reduce economic and ecological cost of the heat production in district heating systems. Development of a decentralized solution lies in the objective of making the load balancing more accessible and attractive for the companies in charge of providing district-heating services. Objectives. This master thesis aims to find a new alternative for implementing decentralized load balancing in district heating systems. Methods. The development of this master thesis involved the review of the state-of-the-art on demand side management in district heating systems and power networks. It also implied the design of the architecture, creation of a software prototype and execution of a simulation of the system to measure the performance in terms of response time. Results. Decentralized demand side management algorithm and communication framework, software architecture description and analysis of the prototype simulation performance. Conclusions. The main conclusion is that it is possible to create a decentralized algorithm that performs load balancing without compromising the individuals’ privacy. It is possible to say that the algorithm shows good levels of performance not only from the system aggregated response time, but also from the individual performance, in terms of memory consumption and CPU consumption.
(+46) 709706206
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Sultan, Sahira. "Cost Evaluation of Building Space Heating; District Heating and Heat Pumps." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-37137.

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Climate change and energy efficiency has become a matter of concern in recent times; therefore, energy efficiency of buildings has drawn major attention. According to the European Commission, EU countries must improve energy efficiency of existing buildings by retrofitting and renovating the buildings. A case study of a renovated commercial building is considered in this degree project. A model of the building is developed in the IDA Indoor Climate and Energy (IDA ICE) software. The model is then augmented to include renovations in the building. Further, the model is simulated in IDA ICE before and after renovations to investigate the impact of renovations on energy consumption of the building for one year. The simulation results indicate peak demands of district heating that occur in the coldest days of the year. The peak demands of energy are expected to increase the district heating cost because they serve as a basis for new pricing model introduced by the energy providers. Hence, it is important from the customer point of view to reduce the peak loads for cost shavings. The project work also provides an insight into the alternative source of energy such as heat pumps to reduce the peak load demands of district heating.
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Urban, Graeme John. "Probabalistic load modelling of electrical demand of residential water heating." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20071.

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Thesis (MScEng)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Energy efficiency and the move to renewable energy resources are of vital importance in growing profitable and sustainable economies. In recent years, greater emphasis has been placed on institutions, companies and individuals to reduce their electrical energy demand through energy management. In an attempt to reduce the demand, the electrical power utility in South Africa, Eskom, has introduced Demand Side Management programs and substantial increases in electricity tariffs. In addition to these, tax incentives have been offered to help off-set the capital costs associated with the investments made in replacing old electrical equipment with new electrically efficient equipment. Thus the need for accurate Measurement and Verification of electrical energy demand reduction, to substantiate fiscal claims, has become imperative. The main purpose of Measurement and Verification is to investigate the actual monetary performance of an energy savings project. Energy savings assessments, based on purely deterministic baseline demand, do not adequately represent the statistical nature of the savings impacts of many practical load systems, as disclosed in a reporting period. This thesis presents the development of a generic probabilistic methodology to determine the demand profiles of preand post-Energy Conservation Measures (ECMs) for practical load systems. The difference between the simulated demand of the pre- and post-ECMs for a particular set of variables represent the electrical demand impact. The electrical demand of the pre- and post-ECMs is defined in terms of Probability Density Functions, and derived using a multivariate kernel density estimation algorithm. The approach is tested using a simulation model of a waterheating geyser implemented in MATLAB. Three different ECMs are simulated using the geyser model and demand density estimation. The results of the demand impacts of the ECMs are presented and evaluated. With regards to possible future research this methodology could be applied to the evaluation of the demand impacts of heat pump technologies and solar water heaters.
AFRIKAANSE OPSOMMING: en die skuif na hernubare energiebronne is van deurslaggewende belang vir die ontwikkeling van winsgewende en volhoubare ekonomieë. Onlangs is meer klem geplaas op instansies, maatskappye en individue om hul aanvraag na energie te verminder met behulp van energiebestuur. In ‘n poging om die aanvraag te verlaag, het Eskom, Suid-Afrika se elektrisiteitsverskaffer, aansienlike elektrisiteitstariefverhogings ingelyf en Aanvraagbestuursprogramme van stapel gestuur. Bykomend hiertoe is belastingaansporings ook aangebied, waarteen kapitale kostes, geassosieer met die vervanging van ou elektriese toerusting met nuwe elektries doeltreffende toerusting, afgeset kan word. Derhalwe het die behoefte aan akkurate Meting en Verifikasie van elektriese energie aanvraagvermindering, om finansiële eise te staaf, noodsaaklik geword. Die hoofdoel van Meting en Verifikasie is om die werklike finansiële prestasie van energiebesparingsprojek te ondersoek soos bekend gemaak word tydens ’n verslagdoeningstydperk. Energiebesparingassesserings wat slegs gebaseer word op die suiwer deterministiese basislyn aanvraag na elektrisiteit, verteenwoordig nie die werklike statistiese aard van die besparingsimpakte van baie praktiese lasstelsels nie. Hierdie tesis stel die ontwikkeling van generiese waarskynlikheids-metodologie voor, om die voor- en na- Energiebesparings-maatreëls se aanvraagprofiele vir sulke praktiese lasstelsels, vas te stel. Die verskil tussen die gesimuleerde aanvraag van die voor- en na- Energiebesparings-maatreëls vir spesifieke stel veranderlikes verteenwoordig die elektriese aanvraag impak. Die voor- en na- Energiebesparings-maatreëls van die energieverbruik profieldata word gedefinieer in terme van Waarskynlikheidsdigtheidsfunksies en afgelei deur gebruik te maak van meerveranderlike kerndigtheidafskattingsalgoritme. Die benadering is getoets deur gebruik te maak van simuleringsmodel van warmwaterstelsel geïmplimenteer in MATLAB. Drie verskillende voor- en na- Energiebesparings-maatreëls is gesimuleer met behulp van die warmwaterstelselmodel en aanvraag digtheidafskatting. Die resultate van die elektriese aanvraag impakte van die voor- en na- Energiebesparings-maatreëls word vervolgens bespreek en geëvalueer. Met betrekking tot moontlike toekomstige navorsing kan hierdie metodologie toegepas word om die aanvraag impakte van hittepomp- en sonwaterverwarmingstegnologieë te evalueer.
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Tabarra, M. "Load factor effects on thermally stratified solar storage tanks." Thesis, De Montfort University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356458.

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Fernandez, del Castillo Lisa. "Design of a novel test bench for induction heating load characterization." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90135.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
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"June 2014." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 87-89).
Magnetic materials used in induction heating applications have nonlinear magnetic properties with respect to field strength and frequency, which can be effectively characterized using experimental techniques. To this end, we present a test bench inverter optimized for induction heating experimentation, capable of driving an inductive load across a 1-100 kHz frequency range with up to 2 kW power. Harmonic distortion of the inverter is minimized with a novel multilevel topology and modulation scheme, thus allowing near-sinusoidal excitations to be obtained at varying field strengths and frequencies. To demonstration the capabilities of the test bench, we characterize the power dissipation of a loaded induction heating coil across a range of frequencies and power levels.
by Lisa Fernandez del Castillo.
S.M.
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Provatas, Spyridon. "An Online Machine Learning Algorithm for Heat Load Forecasting in District Heating Systems." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3475.

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Context. Heat load forecasting is an important part of district heating optimization. In particular, energy companies aim at minimizing peak boiler usage, optimizing combined heat and power generation and planning base production. To achieve resource efficiency, the energy companies need to estimate how much energy is required to satisfy the market demand. Objectives. We suggest an online machine learning algorithm for heat load forecasting. Online algorithms are increasingly used due to their computational efficiency and their ability to handle changes of the predictive target variable over time. We extend the implementation of online bagging to make it compatible to regression problems and we use the Fast Incremental Model Trees with Drift Detection (FIMT-DD) as the base model. Finally, we implement and incorporate to the algorithm a mechanism that handles missing values, measurement errors and outliers. Methods. To conduct our experiments, we use two machine learning software applications, namely Waikato Environment for Knowledge Analysis (WEKA) and Massive Online Analysis (MOA). The predictive ability of the suggested algorithm is evaluated on operational data from a part of the Karlshamn District Heating network. We investigate two approaches for aggregating the data from the nodes of the network. The algorithm is evaluated on 100 runs using the repeated measures experimental design. A paired T-test is run to test the hypothesis that the the choice of approach does not have a significant effect on the predictive error of the algorithm. Results. The presented algorithm forecasts the heat load with a mean absolute percentage error of 4.77\%. This means that there is a sufficiently accurate estimation of the actual values of the heat load, which can enable heat suppliers to plan and manage more effectively the heat production. Conclusions. Experimental results show that the presented algorithm can be a viable alternative to state-of-the-art algorithms that are used for heat load forecasting. In addition to its predictive ability, it is memory-efficient and can process data in real time. Robust heat load forecasting is an important part of increased system efficiency within district heating, and the presented algorithm provides a concrete foundation for operational usage of online machine learning algorithms within the domain.
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Amrhein, Andrew Aloysius. "Induction Heating of Aluminum Cookware." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/77400.

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Induction heating has become a popular alternative to other heat sources for stovetop cooking applications due to performance, efficiency, control response, and safety. The main drawback is that extreme difficulty is encountered when trying to head low-resistivity, non-ferromagnetic metals such as aluminum and copper, which are commonly used for cookware in several societies. The lack of ferromagnetic properties, resulting in no hysteresis dissipation, and low resistivity of such metals results in an impractically low resistance reflected through the work coil. The resultant impedance complicates inverter design, as it is too low to be efficiently driven with conventional inverter topologies. The magnitudes of current involved in exciting this impedance also severely impact the efficiency of the coil and resonant components, requiring extreme care in coil design. This work explores various techniques that have been proposed and/or applied to efficiently heat low-resistivity cookware and the associated limitations. A transformer-coupled series-load-resonant topology driven by a full-bridge inverter is proposed as a means of efficiently heating aluminum cookware within practical design constraints. The experimental circuit is built and successfully tested at an output power of 1.66kW. The procedure of optimizing the work coil for improved efficiency is also presented along with the procedure of measuring coil efficiency. An improved circuit incorporating switch voltage detection to guarantee zero-voltage switching is then built in order to overcome limitations of this design.
Master of Science
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Landis, Mark J. "Development of a Parametric Data-Driven Fixed Shading Device Design Workflow." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553250987067742.

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Books on the topic "Heating load"

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Introduction to heating, ventilation, and air conditioning: How to calculate heating and cooling loads. Troy, Mich: Business News Pub. Co., 1995.

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American Society of Heating, Refrigerating and Air-Conditioning Engineers. Cooling and heating load calculation manual. 2nd ed. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers., 1992.

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D, Spitler Jeffrey, and American Society of Heating, Refrigerating, and Air-Conditioning Engineers., eds. Cooling and heating load calculation manual. 2nd ed. New York: ASHRAE, 1994.

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American Society of Heating, Refrigerating and Air-Conditioning Engineers, ed. Load calculation applications manual. Atlanta: ASHRAE, 2014.

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Spitler, Jeffrey D. Load calculation applications manual. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, 2010.

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Load calculation applications manual. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, 2008.

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Load calculation applications manual. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, 2010.

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Spitler, Jeffrey D. Load calculation applications manual. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, 2010.

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Analyzing field measurements: Air conditioning & heating. 2nd ed. Lilburn, GA: Fairmont Press, 1997.

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American Society of Heating, Refrigerating, and Air-Conditioning Engineers., ed. Annotated guide to load calculation models and algorithms. Atlanta: American Society of Heating, Refrigeratin and Air-Conditioning Engineers, Inc., 1996.

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Book chapters on the topic "Heating load"

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Bianchi, Federico, Francesco Masillo, Alberto Castellini, and Alessandro Farinelli. "XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks." In Machine Learning, Optimization, and Data Science, 601–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_53.

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Jacob, Maria, Cláudia Neves, and Danica Vukadinović Greetham. "Short Term Load Forecasting." In Forecasting and Assessing Risk of Individual Electricity Peaks, 15–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28669-9_2.

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Abstract Electrification of transport and heating, and the integration of low carbon technologies (LCT) is driving the need to know when and how much electricity is being consumed and generated by consumers. It is also important to know what external factors influence individual electricity demand.
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Wang, Ruiting, Fulin Wang, Zhaohan Nan, Minjie Xiao, and Aijun Ding. "Precise Control for Heating Supply to Households Based on Heating Load Prediction." In Environmental Science and Engineering, 855–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9524-6_89.

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Jia, Meng, Chunhua Sun, Shanshan Cao, and Chengying Qi. "District Heating System Load Prediction Using Machine Learning Method." In Environmental Science and Engineering, 581–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9524-6_61.

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Daub, Dennis, Sebastian Willems, Burkard Esser, and Ali Gülhan. "Experiments on Aerothermal Supersonic Fluid-Structure Interaction." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 323–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53847-7_21.

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Abstract Mastering aerothermal fluid-structure interaction (FSI) is crucial for the efficient and reliable design of future (reusable) launch vehicles. However, capabilities in this area are still quite limited. To address this issue, a multidisciplinary experimental and numerical study of such problems was conducted within SFB TRR 40. Our work during the last funding period was focused on studying the effects of moderate and high thermal loads. This paper provides an overview of our experiments on FSI including structural dynamics and thermal effects for configurations in two different flow regimes. The first setup was designed to study the combined effects of thermal and pressure loads. We investigated a range of conditions including shock-wave/boundary-layer interaction (SWBLI) with various incident shock angles leading to, in some cases, large flow separation with high amplitude temperature dependent panel oscillations. The respective aerothermal loads were studied in detail using a rigid reference panel. The second setup allowed us to study the effects of severe heating leading to plastic deformation of the structure. We obtained severe localized heating resulting in partly plastic deformations of more than 12 times the panel thickness. Furthermore, the effects of repeated load cycles were studied.
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Farid, Mohammed, Amar Auckaili, and Gohar Gholamibozanjani. "Energy Saving, Peak Load Shifting and Price-Based Control Heating." In Thermal Energy Storage with Phase Change Materials, 159–61. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780367567699-12.

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Cao, Shanshan, Hua Zhao, Xin Xie, and Xiaolin Liu. "District Heating System Adjustment Theoretical Based on Heat Users’ Real Load." In Lecture Notes in Electrical Engineering, 577–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39581-9_57.

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Bianchi, Federico, Pietro Tarocco, Alberto Castellini, and Alessandro Farinelli. "Convolutional Neural Network and Stochastic Variational Gaussian Process for Heating Load Forecasting." In Machine Learning, Optimization, and Data Science, 244–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_23.

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van Pruissen, Olaf, and René Kamphuis. "Suppressing Peak Load at Simultaneous Demand of Electric Heating in Residential Areas." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 85–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19322-4_10.

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Zhao, Han, Qingsong Wang, Yanfei Su, Yu Wang, Guangzheng Shao, Haodong Chen, and Jinhua Sun. "Experimental Investigation on Glass Cracking for Wind Load Combined with Radiant Heating." In Fire Science and Technology 2015, 255–60. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0376-9_25.

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Conference papers on the topic "Heating load"

1

Fox, B. "Scheduling of heating load." In IEE Colloquium on The New NIE Energy Management System. IEE, 1998. http://dx.doi.org/10.1049/ic:19980476.

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van Deventer, Jan, Jonas Gustafsson, and Jerker Delsing. "Controlling district heating load through prices." In 2011 IEEE International Systems Conference (SysCon). IEEE, 2011. http://dx.doi.org/10.1109/syscon.2011.5929104.

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Dzieniakowski, Maciej A., Jan Fabianowski, and Robert Ibach. "LCL-load modular converter for induction heating." In 2008 13th International Power Electronics and Motion Control Conference (EPE/PEMC 2008). IEEE, 2008. http://dx.doi.org/10.1109/epepemc.2008.4635573.

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Kral, Erik, Alena Kostalova, Petr Capek, and Lubomir Vasek. "Algorithm for Central Heating Heat Load Modelling." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00279.

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Hietaharju, Petri, and Mika Ruusunen. "Peak Load Cutting in District Heating Network." In Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016. Linköping University Electronic Press, 2018. http://dx.doi.org/10.3384/ecp1714299.

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Mokeev, Vladimir V. "Prediction of Heating Load and Cooling Load of Buildings Using Neural Network." In 2019 International Ural Conference on Electrical Power Engineering (UralCon). IEEE, 2019. http://dx.doi.org/10.1109/uralcon.2019.8877655.

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Du, Wei, Ming Zeng, Yuanfei Li, Shengyuan Zhong, and Xiaoyuan Wang. "The heating load control method of load aggregators based on cluster analysis." In 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2020. http://dx.doi.org/10.1109/appeec48164.2020.9220357.

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Savage, H., J. Kennedy, B. Fox, and D. Flynn. "Managing variability of wind energy with heating load control." In 2008 43rd International Universities Power Engineering Conference (UPEC). IEEE, 2008. http://dx.doi.org/10.1109/upec.2008.4651571.

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Baciu, Ionel H., Adrian Taut, Ovidiu Pop, and Serban Lungu. "Advanced simulation of load variation in induction heating systems." In 2009 32nd International Spring Seminar on Electronics Technology (ISSE). IEEE, 2009. http://dx.doi.org/10.1109/isse.2009.5207004.

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Li, Jingang, Wenxian Luo, and Chen Chen. "Research on Load Parameter Identification of the Induction Heating." In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2019. http://dx.doi.org/10.1109/iciea.2019.8834357.

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Reports on the topic "Heating load"

1

Burdick, Arlan. Strategy Guideline. Accurate Heating and Cooling Load Calculations. Office of Scientific and Technical Information (OSTI), June 2011. http://dx.doi.org/10.2172/1219203.

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Burdick, A. Strategy Guideline: Accurate Heating and Cooling Load Calculations. Office of Scientific and Technical Information (OSTI), June 2011. http://dx.doi.org/10.2172/1018100.

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Rice, C. Keith, Bo Shen, and Som S. Shrestha. An analysis of representative heating load lines for residential HSPF ratings. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1214506.

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Rice, C. Keith, Bo Shen, and Som S. Shrestha. Revised Heating Load Line Analysis: Addendum to ORNL/TM-2015/281. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1287036.

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Cui, Borui, Jaewan Joe, Jeffrey Munk, Jian Sun, and Teja Kuruganti. Load Flexibility Analysis of Residential HVAC and Water Heating and Commercial Refrigeration. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1564190.

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Poerschke, Andrew. Risk Assessment of Heating, Ventilating, and Air-Conditioning Strategies in Low-Load Homes. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1239974.

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Poerschke, Andrew. Risk Assessment of Heating, Ventilating, and Air-Conditioning Strategies in Low-Load Homes. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1240496.

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Bin, Shen. Test methods for mechanical load on support of close-coupled solar water heating systems. IEA SHC Task 57, September 2018. http://dx.doi.org/10.18777/ieashc-task57-2018-0007.

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Hirst, E., and R. Goeltz. Electricity use for residential space heating: comparison of the Princeton Scorekeeping Method with end-use load data. Office of Scientific and Technical Information (OSTI), April 1986. http://dx.doi.org/10.2172/5857556.

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Barrett, Emily. The Investigation and Optimization of a Two-Heat-Pump System Incorporating Thermal Storage for Shaping Residential Heating Load. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3020.

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