Academic literature on the topic 'Air conditioning – Energy consumption – Data processing'

Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processors and room temperature. Specifically, our cyber-physical communication model dynamically identifies processing states in data center while implying a suitable air-conditioning temperature level. The information is then used by administration to fine-tune the room temperature according to the current processing activities. Our automated triggering approach aims to improve edge computing performance with cost-effective energy consumption. Simulation experiments show that our cyber-physical communication achieves better energy consumption and resource utilization compared with other cooling model.

Control approaches of heating, ventilation and air conditioning systems in buildings have been proposed in the past years for minimizing energy consumption and maintaining occupants’ comfort. However, recent studies have shown that context-driven control approaches using Internet of things and data stream processing technologies could further improve energy saving in heating, ventilation and air conditioning systems. In this article, an intelligent control approach using a state feedback technique is introduced to regulate the heating, ventilation and air conditioning system according to the actual context. The proposed thermal state feedback control was then implemented and deployed in our EEBLab to study its effectiveness in a real-setting scenario. The performance of the proposed control was evaluated in a real test-site by deploying a control card that links the controller with the heating, ventilation and air conditioning system. A smart mobile application for real feedback control was also developed and deployed to dynamically adapt the controller to context’s changes. The mobile application and the heating, ventilation and air conditioning system communicate and exchange data under a data acquisition and visualization platform. In this article, a holistic platform that combines Internet of things and data stream processing technologies was developed and deployed in a real-setting scenario. Experiments have been performed, and results are reported to demonstrate the effectiveness and usefulness of the proposed approach in terms of energy saving while maintaining a comfortable room temperature. The proposed state feedback control outperforms the proportional–integral–derivative and ON/OFF approaches in terms of energy consumption while providing acceptable thermal comfort by allowing a neutral thermal sensation with ± 0.30 of predictive mean vote and less than 7% of predicted percentage of dissatisfaction.

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

<p>Building energy management systems (BEMS) are critical tools for managing and controlling a facility's technical systems and services, such as lighting, ventilation, heating, and air conditioning, to ensure that the building operates at peak efficiency while decreasing energy waste. The Mabini Building at De La Salle Lipa has nearly a hundred rooms, 70 of which are used by college students for lecture and laboratory classes. From 7:30 a.m. to 9:00 p.m., these rooms are available. In a daily class schedule, air conditioning units and lights are used an average of 10 hours per day, while fans and power outlets are used an average of 5 hours. Even when no classes are being held, the aforementioned equipment is frequently left open in these rooms. The researchers created and constructed an IoT-based energy monitoring system to monitor and control the lights and outlets in a room. The system will also record the number of kilowatt-hours (kWh) consumed. The system employs NodeMCU, current, and voltage sensors, a Raspberry Pi 3, and the school's existing network to send and receive data from the server. The building administrator will use the collected data to give consumption statistics and reduce the carbon footprint.</p>

In this paper, Internet of Things (IoT) and artificial intelligence (AI) are employed to solve the issue of energy consumption in a case study of an education laboratory. IoT enables deployment of AI approaches to establish smart systems and manage the sensor signals between different equipment based on smart decisions. As a result, this paper introduces the design and investigation of an experimental building management system (BMS)-based IoT approach to monitor status of sensors and control operation of loads to reduce energy consumption. The proposed BMS is built on integration between a programmable logic controller (PLC), a Node MCU ESP8266, and an Arduino Mega 2560 to perform the roles of transferring and processing data as well as decision-making. The system employs a variety of sensors, including a DHT11 sensor, an IR sensor, a smoke sensor, and an ultrasonic sensor. The collected IoT data from temperature sensors are used to build an artificial neural network (ANN) model to forecast the temperature inside the laboratory. The proposed IoT platform is created by the ThingSpeak platform, the Bylink dashboard, and a mobile application. The experimental results show that the experimental BMS can monitor the sensor data and publish the data on different IoT platforms. In addition, the results demonstrate that operation of the air-conditioning, lighting, firefighting, and ventilation systems could be optimally monitored and managed for a smart system with an architectural design. Furthermore, the results prove that the ANN model can perform a distinct temperature forecasting process based on IoT data.

The Office of the National Broadcasting and Telecommunications Commission has reported that, from 2014 to 2018, Thailand’s internet usage has grown six-fold to 3.3 million terabytes per annum. This market trend highlights one of the policies of Thailand 4.0, with the aim of making Thailand a hub for information transfer in ASEAN. As a result, there will be a massive demand growth for data storage facilities in the near future. Data centres are regarded as the brain and heart of the digital industry and are essential for facilitating businesses in organising, processing, storing and disseminating large amounts of data. As the energy demand for equipment cooling contributes to over 37% of the total energy consumption, the data centres of the world’s leading companies, such as Amazon, Google, Microsoft and Facebook, are generally located in cold climate zones, such as Iceland, in order to reduce operating costs for cooling. Due to this reason, the possibility of data centres in Thailand is limited. Beneficially, PTTLNG, as the first liquified natural gas (LNG) terminal in Thailand, has processed the import, receiving, storage and regasification of LNG. The high abundance of cold energy inherently presented in LNG is normally lost to the surroundings during regasification. Presently, PTTLNG’s LNG receiving terminal utilises a heat exchanger with propane as an intermediate fluid to transfer cold energy from LNG to water. This cold energy, in the form of cold water, is then used in several projects within the LNG receiving terminal: (1) production of electricity via an organic Rankine cycle capacity of 5 MWh; (2) cooling the air inlet of gas turbine generators to increase the generator efficiency; (3) replacing refrigerant heating, ventilation and air conditioning systems within buildings; (4) development of winter plantations with precision agriculture to replace imported products. Therefore, this study focuses on the potential and future use for LNG cold energy by performing a thermodynamic and economic analysis of the use of LNG cold energy as a source to produce cold water at 7 °C, with the total cold energy of 27.77 to 34.15 MW or 7934 t to 9757 t of refrigeration depending on the target pressure of the natural gas to replace the conventional cooling system of data centres. This research has the potential to reduce the cooling operation costs of data centres by more than USD 9.87 million per annum as well as CO2 emissions by 34,772 t per annum. In an economic study, this research could lead to a payback period of 7 years with IRR 13% for the LNG receiving terminal and a payback period of 2.21 years with IRR 45% for digital companies.

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

In this paper, the Unicom's medium-sized IDC room in a city of northern China is the research object for the study. Based on field research of the room refrigeration conditions, data center room air conditioning system is carried out to optimize and for energy conservation research. Through the analytic methods of energy saving-technology, the best energy saving solutions is explored.

Abstract Global warming has set in motion a trend for cutting energy costs to reduce the carbon footprint. Reducing energy consumption, cutting greenhouse gas emissions and eliminating energy wastage are among the main goals of the European Union (EU). The buildings sector is the largest user of energy and CO2 emitter in the EU, estimated at approximately 40% of the total consumption. According to the International Panel on Climate Change, 30% of the energy used in buildings could be reduced with net economic benefits by 2030. At the same time, indoor air quality is recognized more and more as a distinct health hazard. Because of these two factors, energy efficiency and healthy housing have become active topics in international research. The main aims of this thesis were to study and develop a wireless building monitoring and control system that will produce valuable information and services for end-users using computational methods. In addition, the technology developed in this thesis relies heavily on building automation systems (BAS) and some parts of the concept termed the “Internet of Things” (IoT). The data refining process used is called knowledge discovery from data (KDD) and contains methods for data acquisition, pre-processing, modeling, visualization and interpreting the results and then sharing the new information with the end-users. In this thesis, four examples of data analysis and knowledge deployment are presented. The results of the case studies show that innovative use of computational methods provides a good basis for researching and developing new information services. In addition, the data mining methods used, such as regression and clustering completed with efficient data pre-processing methods, have a great potential to process a large amount of multivariate data effectively. The innovative and effective use of digital information is a key element in the creation of new information services. The service business in the building sector is significant, but plenty of new possibilities await capable and advanced companies or organizations. In addition, end-users, such as building maintenance personnel and residents, should be taken into account in the early stage of the data refining process. Furthermore, more advantages can be gained by courageous co-operation between companies and organizations, by utilizing computational methods for data processing to produce valuable information and by using the latest technologies in the research and development of new innovations
Tiivistelmä Rakennus- ja kiinteistösektori on suurin fossiilisilla polttoaineilla tuotetun energian käyttäjä. Noin 40 prosenttia kaikesta energiankulutuksesta liittyy rakennuksiin, rakentamiseen, rakennusmateriaaleihin ja rakennuksien ylläpitoon. Ilmastonmuutoksen ehkäisyssä rakennusten energiankäytön vähentämisellä on suuri merkitys ja rakennuksissa energiansäästöpotentiaali on suurin. Tämän seurauksena yhä tiiviimpi ja energiatehokkaampi rakentaminen asettaa haasteita hyvän sisäilman laadun turvaamiselle. Näistä seikoista johtuen sisäilman laadun tutkiminen ja jatkuvatoiminen mittaaminen on tärkeää. Väitöskirjan päätavoitteena on kuvata kehitetty energiankulutuksen ja sisäilman laadun monitorointijärjestelmä. Järjestelmän tuottamaa mittaustietoa on jalostettu eri loppukäyttäjiä palvelevaan muotoon. Tiedonjalostusprosessi koostuu tiedon keräämisestä, esikäsittelystä, tiedonlouhinnasta, visualisoinnista, tulosten tulkitsemisesta ja oleellisen tiedon välittämisestä loppukäyttäjille. Aineiston analysointiin on käytetty tiedonlouhintamenetelmiä, kuten esimerkiksi klusterointia ja ennustavaa mallintamista. Väitöskirjan toisena tavoitteena on tuoda esille jatkuvatoimiseen mittaamiseen liittyviä haasteita sekä rohkaista yrityksiä ja organisaatioita käyttämään tietovarantoja monipuolisemmin ja tehokkaammin. Väitöskirja pohjautuu viiteen julkaisuun, joissa kuvataan kehitetty monitorointijärjestelmä, osoitetaan tiedonjalostusprosessin toimivuus erilaisissa tapauksissa ja esitetään esimerkkejä kuhunkin prosessivaiheeseen soveltuvista laskennallisista menetelmistä. Julkaisuissa on kuvattu energiankulutuksen ja sisäilman laadun informaatiopalvelu sekä sisäilman laatuun liittyviä data-analyysejä omakoti- ja kerrostaloissa sekä koulurakennuksissa. Innovatiivinen digitaalisen tiedon hyödyntäminen on avainasemassa kehitettäessä uusia informaatiopalveluita. Kiinteistöalalle on kehitetty lukuisia informaatioon pohjautuvia palveluita, mutta ala tarjoaa edelleen hyviä liiketoimintamahdollisuuksia kyvykkäille ja kehittyneille yrityksille sekä organisaatioille

Indiana University-Purdue University Indianapolis (IUPUI)
Heating, ventilation, and air conditioning (HVAC) is the technology responsible to maintain temperature levels and air quality in buildings to certain standards. In a commercial setting, HVAC systems accounted for more than 50% of the total energy cost of the building in 2013 [13]. New control methods are always being worked on to improve the effectiveness and efficiency of the system. These control systems include model predictive control (MPC), evolutionary algorithm (EA), evolutionary programming (EP), and proportional-integral-derivative (PID) controllers. Such control tools are used on new HVAC system to ensure the ultimate efficiency and ensure the comfort of occupants. However, there is a need for a system that can monitor the energy performance of the HVAC system and ensure that it is operating in its optimal operation and controlled as expected. In this thesis, an air handling unit (AHU) of an HVAC system was modeled to analyze its performance using real data collected from an operating AHU using a wireless monitoring system. The purpose was to monitor the AHU's performance, analyze its key parameters to identify flaws, and evaluate the energy waste. This system will provide the maintenance personnel to key information to them to act for increasing energy efficiency. The mechanical model was experimentally validated first. Them a baseline operating condition was established. Finally, the system under extreme weather conditions was evaluated. The AHU's subsystem performance, the energy consumption and the potential wastes were monitored and quantified. The developed system was able to constantly monitor the system and report to the maintenance personnel the information they need. I can be used to identify energy savings opportunities due to controls malfunction. Implementation of this system will provide the system's key performance indicators, offer feedback for adjustment of control strategies, and identify the potential savings. To further verify the capabilities of the model, a case study was performed on an air handling unit on campus for a three month monitoring period. According to the mechanical model, a total of 63,455 kWh can be potentially saved on the unit by adjusting controls. In addition the mechanical model was able to identify other energy savings opportunities due to set point changes that may result in a total of 77,141 kWh.

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
A large amount of energy used by the world comes from buildings’ energy consumption. HVAC (Heat, Ventilation, and Air Conditioning) systems are the biggest offenders when it comes to buildings’ energy consumption. It is important to provide environmental comfort in buildings but indoor wellbeing is directly related to an increase in energy consumption. This dilemma creates a huge opportunity for a solution that balances occupant comfort and energy consumption. Within this context, the Ambiosensing project was launched to develop a complete energy management system that differentiates itself from other existing commercial solutions by being an inexpensive and intelligent system. The Ambiosensing project focused on the topic of Time Series Forecasting to achieve the goal of creating predictive models to help the energy management system to anticipate indoor environmental scenarios. A good approach for Time Series Forecasting problems is to apply Machine Learning, more specifically Deep Learning. This work project intends to investigate and develop Deep Learning and other Machine Learning models that can deal with multivariate Time Series Forecasting, to assess how well can a Deep Learning approach perform on a Time Series Forecasting problem, especially, LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNN) and to establish a comparison between Deep Learning and other Machine Learning models like Linear Regression, Decision Trees, Random Forest, Gradient Boosting Machines and others within this context.

The textbook considers the issue of assessing the heat and humidity state of air in the processes of its processing in various systems, provides requirements for air protection means, taking into account their environmental friendliness, shows ways of energy saving in cooling, heating and year-round air conditioning systems, as well as when protecting the atmosphere from harmful emissions. The way of energy saving with individual thermal protection of the operator by means of local cooling during air treatment in an irrigated intensified nozzle is shown and recommendations for reducing its material consumption are developed. The method and means of reducing the toxicity of emissions of tractor internal combustion engines during its operation in rooms of limited volume by water vapor humidification of the fuel-air mixture are demonstrated. The ways of noise reduction of air protection systems are shown. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students studying in the specialties "Ground transport and technical means", "Operation of transport and technological machines and complexes", "Power engineering", "Ground transport and technological complexes", "Refrigeration, cryogenic equipment and life support systems", "Technosphere safety", "Ecology and nature management".

Economy and mankind are inextricably interlinked. Just as the economy or the production of material wealth is unimaginable without a man, so human existence and development are impossible without the wealth created in the economy. Shortly, both the goal and the means of achieving and realization of the economy are still the human resources. People have long ago noticed that it was the economy that created livelihoods, and the delays in their production led to the catastrophic events such as hunger, poverty, civil wars, social upheavals, revolutions, moral degeneration, and more. Therefore, the special interest of people in understanding the regulatory framework of the functioning of the economy has existed and exists in all historical epochs [A. Sisvadze. Economic theory. Part One. 2006y. p. 22]. The system of economic disciplines studies economy or economic activities of a society. All of them are based on science, which is currently called economic theory in the post-socialist space (the science of economics, the principles of economics or modern economics), and in most countries of the world - predominantly in the Greek-Latin manner - economics. The title of the present book is also Modern Economics. Economics (economic theory) is the science that studies the efficient use of limited resources to produce and distribute goods and services in order to satisfy as much as possible the unlimited needs and demands of the society. More simply, economics is the science of choice and how society manages its limited resources. Moreover, it should be emphasized that economics (economic theory) studies only the distribution, exchange and consumption of the economic wealth (food, beverages, clothing, housing, machine tools, computers, services, etc.), the production of which is possible and limited. And the wealth that exists indefinitely: no economic relations are formed in the production and distribution of solar energy, air, and the like. This current book is the second complete updated edition of the challenges of the modern global economy in the context of the coronary crisis, taking into account some of the priority directions of the country's development. Its purpose is to help students and interested readers gain a thorough knowledge of economics and show them how this knowledge can be applied pragmatically (professionally) in professional activities or in everyday life. To achieve this goal, this textbook, which consists of two parts and tests, discusses in simple and clear language issues such as: the essence of economics as a science, reasons for origin, purpose, tasks, usefulness and functions; Basic principles, problems and peculiarities of economics in different economic systems; Needs and demand, the essence of economic resources, types and limitations; Interaction, mobility, interchangeability and efficient use of economic resources. The essence and types of wealth; The essence, types and models of the economic system; The interaction of households and firms in the market of resources and products; Market mechanism and its elements - demand, supply and price; Demand and supply elasticity; Production costs and the ways to reduce them; Forms of the market - perfect and incomplete competition markets and their peculiarities; Markets for Production Factors and factor incomes; The essence of macroeconomics, causes and importance of origin; The essence and calculation of key macroeconomic indicators (gross national product, gross domestic product, net national product, national income, etc.); Macroeconomic stability and instability, unemployment, inflation and anti-inflationary policies; State regulation of the economy and economic policy; Monetary and fiscal policy; Income and standard of living; Economic Growth; The Corona Pandemic as a Defect and Effect of Globalization; National Economic Problems and New Opportunities for Development in the conditions of the Coronary Crisis; The Socio-economic problems of moral obsolescence in digital technologies; Education and creativity are the main solution way to overcome the economic crisis caused by the coronavirus; Positive and negative effects of tourism in Georgia; Formation of the middle class as a contributing factor to the development of tourism in Georgia; Corporate culture in Georgian travel companies, etc. The axiomatic truth is that economics is the union of people in constant interaction. Given that the behavior of the economy reflects the behavior of the people who make up the economy, after clarifying the essence of the economy, we move on to the analysis of the four principles of individual decision-making. Furtermore, the book describes how people make independent decisions. The key to making an individual decision is that people have to choose from alternative options, that the value of any action is measured by the value of what must be given or what must be given up to get something, that the rational, smart people make decisions based on the comparison of the marginal costs and marginal returns (benefits), and that people behave accordingly to stimuli. Afterwards, the need for human interaction is then analyzed and substantiated. If a person is isolated, he will have to take care of his own food, clothes, shoes, his own house and so on. In the case of such a closed economy and universalization of labor, firstly, its productivity will be low and, secondly, it will be able to consume only what it produces. It is clear that human productivity will be higher and more profitable as a result of labor specialization and the opportunity to trade with others. Indeed, trade allows each person to specialize, to engage in the activities that are most successful, be it agriculture, sewing or construction, and to buy more diverse goods and services from others at a relatively lower price. The key to such human interactions is that trade is mutually beneficial; That markets are usually the good means of coordination between people and that the government can improve the results of market functioning if the market reveals weakness or the results of market functioning are not fair. Moroever, it also shows how the economy works as a whole. In particular, it is argued that productivity is a key determinant of living standards, that an increase in the money supply is a major source of inflation, and that one of the main impediments to avoiding inflation is the existence of an alternative between inflation and unemployment in the short term, that the inflation decrease causes the temporary decline in unemployement and vice versa. The Understanding creatively of all above mentioned issues, we think, will help the reader to develop market economy-appropriate thinking and rational economic-commercial-financial behaviors, to be more competitive in the domestic and international labor markets, and thus to ensure both their own prosperity and the functioning of the country's economy. How he/she copes with the tasks, it is up to the individual reader to decide. At the same time, we will receive all the smart useful advices with a sense of gratitude and will take it into account in the further work. We also would like to thank the editor and reviewers of the books. Finally, there are many things changing, so it is very important to realize that the XXI century has come: 1. The century of the new economy; 2. Age of Knowledge; 3. Age of Information and economic activities are changing in term of innovations. 1. Why is the 21st century the century of the new economy? Because for this period the economic resources, especially non-productive, non-recoverable ones (oil, natural gas, coal, etc.) are becoming increasingly limited. According to the World Energy Council, there are currently 43 years of gas and oil reserves left in the world (see “New Commersant 2007 # 2, p. 16). Under such conditions, sustainable growth of real gross domestic product (GDP) and maximum satisfaction of uncertain needs should be achieved not through the use of more land, labor and capital (extensification), but through more efficient use of available resources (intensification) or innovative economy. And economics, as it was said, is the science of finding the ways about the more effective usage of the limited resources. At the same time, with the sustainable growth and development of the economy, the present needs must be met in a way that does not deprive future generations of the opportunity to meet their needs; 2. Why is the 21st century the age of knowledge? Because in a modern economy, it is not land (natural resources), labor and capital that is crucial, but knowledge. Modern production, its factors and products are not time-consuming and capital-intensive, but science-intensive, knowledge-intensive. The good example of this is a Japanese enterprise (firm) where the production process is going on but people are almost invisible, also, the result of such production (Japanese product) is a miniature or a sample of how to get the maximum result at the lowest cost; 3. Why is the 21st century the age of information? Because the efficient functioning of the modern economy, the effective organization of the material and personal factors of production largely depend on the right governance decision. The right governance decision requires prompt and accurate information. Gone are the days when the main means of transport was a sailing ship, the main form of data processing was pencil and paper, and the main means of transmitting information was sending letters through a postman on horseback. By the modern transport infrastructure (highways, railways, ships, regular domestic and international flights, oil and gas pipelines, etc.), the movement of goods, services and labor resoucres has been significantly accelerated, while through the modern means of communication (mobile phone, internet, other) the information is spreading rapidly globally, which seems to have "shrunk" the world and made it a single large country. The Authors of the book: Ushangi Samadashvili, Doctor of Economic Sciences, Associate Professor of Ivane Javakhishvili Tbilisi State University - Introduction, Chapters - 1, 2, 3, 4, 5, 6, 9, 10, 11,12, 15,16, 17.1,18 , Tests, Revaz Shengelia, Doctor of Economics, Professor of Georgian Technical University, Chapters_7, 8, 13. 14, 17.2, 17.4; Zhuzhuna Tsiklauri - Doctor of Economics, Professor of Georgian Technical University - Chapters 13.6, 13.7,17.2, 17.3, 18. We also thank the editor and reviewers of the book.

AbstractData centres are complex energy demanding environments. The number of data centres and thereby their energy consumption around the world is growing at a rapid rate. Cooling the servers in the form of air conditioning forms a major part of the total energy consumption in data centres and thus there is an urgent need to develop alternative energy efficient cooling technologies. Liquid cooling systems are one such solution which are in their early developmental stage. In this article, the use of Computational Fluid Dynamics (CFD) to further improve the design of liquid-cooled systems is discussed by predicting temperature distribution and heat exchanger performance. A typical 40 kW rack cabinet with rear door fans and an intermediate air–liquid heat exchanger is used in the CFD simulations. Steady state Reynolds-Averaged Navier–Stokes modelling approach with the RNG K-epsilon turbulence model and the Radiator boundary conditions were used in the simulations. Results predict that heat exchanger effectiveness and uniform airflow across the cabinet are key factors to achieve efficient cooling and to avoid hot spots. The fundamental advantages and limitations of CFD modelling in liquid-cooled data centre racks were also discussed. In additional, emerging technologies for data centre cooling have also been discussed.

The internet of things (IoT) is a system consisting of computing, mechanical, and electronic devices, which are having ability to transfer data in network without human interaction. The sensors used in IoT collect and transfer the data to the cloud, which is further processed using software to perform an action. The IoT is one of the fastest growing industries, and in recent years, it is most widely used in HVAC systems in residential and commercial applications to reduce the energy consumption as building consumes by approximately 40% of total energy. The IoT reduces the energy consumption of the building by optimizing the process variables of HVAC system components, increases life of system components, enhances the comfort of the occupants, and provides remote control of the system. However, there are challenges in data security and privacy, and also there is a lack of IoT platforms specifically oriented towards the proper processing, management, and analysis of such large and diverse data.

The enclosure, lighting and equipment of existing residential buildings have been determined. Therefore, the occupancy behavior has a great influence on the energy consumption of air conditioning in the existing residential buildings. In this paper, a residential building was selected as the research object, and the long- term energy consumption of the building was sorted out. Then through energy calculation and correction with the actual building energy consumption data, the air conditioning energy consumption characteristics which are related to personnel timetable, air conditioning control mode, room setting temperature and room temperature range are obtained. The characteristics have a good matching degree with the actual energy consumption, which can basically reflect the habit of using air conditioning for the occupants. The characteristics can help occupants to know the energy-saving potential and promote behavioral energy-saving.

Green energy paradigm has been gaining popularity in the computing system from the software, hardware, infrastructure and application perspectives. Within that concept, data center greening is of utmost importance at the moment since data centers are one of the most energy conserving elements. Data centers are seen as the technology era's black energy-swallowing secret. Reducing energy consumption at data centers can reduce carbon footprint effect tremendously. Not addressing the issue immediately will lead to significant energy usage by data centers and will hinder the growth of data centers. The call for sustainable energy efficient data center leads to venturing into data center green computing. The green computing concept can be achieved by using several methods adopted by researchers including renewable energy, virtualization through cloud computing, proper cooling system, identifying suitable location to harvest energy whilst reducing the need for air-conditioning and employing suitable networking and information technology infrastructure. This paper focuses into several approaches used by researches to reduce energy consumption at data centers while deploying efficient database management system. This paper differs from others in the literature by giving some suitable solutions by looking into a hybrid model for green computing in data centers.

Indoor air quality (IAQ) is among the topmost environmental hazards associated with the health of human beings. The concentrations of indoor pollutants could be several times more than outdoors. Increasing environmental pollution and global warming are also responsible for climate change. Variations in climatic conditions also add to the worsening of IAQ. The majority of time is spent indoors and adequate ventilation, thermal performance and desirable IAQ are important parameters of concern in indoor settings. Usage of HVAC (heating, ventilation, air conditioning) equipment accounts for the huge consumption of energy and reduced energy consumption can be met by reduced air circulation leading to more airtight buildings which compromise the air quality and health of inhabitants. Several strategies have been devised and being implemented to monitor indoor air quality. Smart environments are insidious systems consisting of integrable net-aware devices. Smart environments are augmented with computational resources providing information and services when and where needed. Over the last few years, IAQ monitoring has developed into smart environment monitoring (SEM) which is based on the internet of things (IoT) and the development of sensor technology. This chapter is an attempt to summarize the automated, computational aids and machine learning techniques that can predict the IAQ in smart environment. It is imperative to know the pollutants and factors governing the IAQ and the chapter has critically analyzed the available technological interventions based on IoT like sensors, Fuzzy logic controller and cloud computing technology which aid in the prediction of air quality in smart environment. Different types of sensors including infrared and electrochemical cells, Metal oxide semiconductor (MOS) gas sensor along with their principle has been discussed in context to IAQ. Recent developments in the field like the usage of the fuzzy logic controller for the calculation of air quality index by combining PM10, PM2.5, CO, and NO2 etc. has also been explored. The information can be utilized in dynamic situations to suggest alternative methods https://worldpopulationreview.com/world-cities/lucknow-population for the improvement of air quality which can be influenced by artificial intelligence and machine learning for futuristic predictions. However, there are some challenges as well including the development of systems working on a real-time basis and evaluation of the impact of different pollutants in diverse geographic conditions and variable living set-ups by highly accurate and calibrated systems. Nevertheless, as compared to the conventional solutions which predict IAQ instantly, the computational predictions furnish futuristic data and imminent crucial changes in the indoor air quality to implement anticipatory measures to prevent hazardous health impacts. Nevertheless there are several challenges like data security, data conversion, and connectivity issues etc. which have been discussed in the chapter.

This chapter discusses the environmental assessment of telecommunications switching centers (telephone exchanges), based on the experience gained by Climate Associates Limited (CAL) and K8T on contracts in the UK and Ireland over the last few years. CAL has been asked to assess the energy efficiency of telephone exchanges and make recommendation on how their energy efficiency could be improved. Although we are not able to disclose details that may be commercially in confidence, this chapter draws out some general principles on the energy efficiency of telecommunications switching centers, taking into account the electricity demand of the equipment, the energy performance of the buildings housing it, the air conditioning needed to cool it, and the electrical systems used to power it, with a focus on how this could be improved. Reference is made to assessment standards such as ITU-T L.1310 Energy efficiency measurement and metrics for telecommunication network and ITU-T L.1300 Best Practices for Green Data Centers. Dr. Keith Dickerson and Dr. David Faulkner have both been active in the development of standards for environmental assessment in the European Telecommunications Standards Institute (ETSI) and the International Telecommunications Union (ITU) over the past 10 years and hold leadership positions in these bodies. Dr. Paul Kingston has an excellent track record in the modeling and assessment of power consumption to optimize design of the built environment. Acknowledgement is given to BT for permission to publish the results of this study. The results are based primarily on the study of a single telephone exchange and may not be valid for all exchanges of this type in the UK.

This chapter discusses the environmental assessment of telecommunications switching centers (telephone exchanges), based on the experience gained by Climate Associates Limited (CAL) and K8T on contracts in the UK and Ireland over the last few years. CAL has been asked to assess the energy efficiency of telephone exchanges and make recommendation on how their energy efficiency could be improved. Although we are not able to disclose details that may be commercially in confidence, this chapter draws out some general principles on the energy efficiency of telecommunications switching centers, taking into account the electricity demand of the equipment, the energy performance of the buildings housing it, the air conditioning needed to cool it, and the electrical systems used to power it, with a focus on how this could be improved. Reference is made to assessment standards such as ITU-T L.1310 Energy efficiency measurement and metrics for telecommunication network and ITU-T L.1300 Best Practices for Green Data Centers. Dr. Keith Dickerson and Dr. David Faulkner have both been active in the development of standards for environmental assessment in the European Telecommunications Standards Institute (ETSI) and the International Telecommunications Union (ITU) over the past 10 years and hold leadership positions in these bodies. Dr. Paul Kingston has an excellent track record in the modeling and assessment of power consumption to optimize design of the built environment. Acknowledgement is given to BT for permission to publish the results of this study. The results are based primarily on the study of a single telephone exchange and may not be valid for all exchanges of this type in the UK.

The rapid growth in cloud computing, the Internet of Things (IoT), and data processing via Machine Learning (ML), have greatly increased our need for computing resources. Given this rapid growth, it is expected that data centers will consume more and more of our global energy supply. Improving their energy efficiency is therefore crucial. One of the biggest sources of energy consumption is the energy required to cool the data centers, and ensure that the servers stay within their intended operating temperature range. Indeed, about 40% of a data center’s total power consumption is for air conditioning[1]. Here, we study how the server air inlet and outlet, as well as the CPU, temperatures depend upon server loads typical of real Internet Protocol (IP) traces. The trace data used here are from Google clusters and include the times, job and task ID, as well as the number and usage of CPU cores. The resulting IT loads are distributed using standard load-balancing methods such as Round Robin (RR) and the CPU utilization method. Experiments are conducted in the Data Center Laboratory (DCL) at the Georgia Institute of Technology to monitor the server outlet air temperature, as well as real-time CPU temperatures for servers at different heights within the rack. Server temperatures were measured by on-line temperature monitoring with Xbee, Raspberry PI, Arduino, and hot-wire anemometers. Given that the temperature response varies with server position, in part due to spatial variations in the cooling airflow over the rack inlet and the server fan speeds, a new load-balancing approach that accounts for spatially varying temperature response within a rack is tested and validated in this paper.

With the growth of Internet-based services, data centers must deal with increasing data traffic. Load-balancing technologies can help data centers process data effectively and stably; however, the current load-balancing methods do not take into account the heat generated by servers. Excessive heat can increase the failure rate of IT devices and the energy consumption of air conditioning systems, both of which lead to higher data center maintenance costs. This paper aims to simultaneously increase the coefficient of performance (COP) of the data center’s air-conditioning equipment and decrease the semiconductor-based equipment failure rate. To do so — and, consequently, reduce the operation and maintenance costs — we propose a novel request distribution system based on server-temperature and evaluate the proposed system by creating a thermal model of a data center. As a result, it is suggested that using the proposed load-balancing method the semiconductor failure rate can be reduced by 32 % when compared with the common round-robin distribution method, and by 19 % when compared with a load-balancing method based on CPU utilization. Moreover, the COP of the air-conditioning equipment obtained with the proposed method is recognized to be higher than those obtained with either the round-robin or the CPU-utilization-based methods.

Electricity for heating, ventilation, and air condition (HVAC) machines takes up a large percentage of energy consumption in the buildings and thus in turn, a large portion of the energy monetary cost. Optimization of air conditioners use throughout the day will reduce energy consumption and expenditure. This study introduces a second-order differential equation model to capture the indoor temperature dynamics of a building. An experimental test bed is developed to collect a set of indoor/outdoor temperature and sunlight data. Using a least-squares-based system identification process, the model parameters are identified and checked through simulation. Optimization of the room temperature is then determined by solving a mixed-integer quadratic programming problem in relation to the hourly-updated energy prices. Mixed-integer quadratic programming solution is compared to a two-point thermostatic control system. A hybrid solution compromising the quadratic programming algorithm and the conventional thermostatic control scheme is proposed as a tractable approach for the near-optimal energy management of the system.

The use of Prismatic Skylights and its effects as a passive Energy Conservation Strategy in “Residential” and “Big Box Commercial Buildings” in hot and humid climate has been evaluated throughout this project. The potential benefits of using skylights reside in the fact that it reduces electrical lighting necessities but at the same time it contributes to an upsurge of the Cooling Loads of the conditioned space. Acknowledging the impact of skylights is fundamental to elaborate an optimized design of a building’s energy efficient mechanical system. To reach a sound conclusion, the evaluated buildings were modeled and their performance was simulated using the Department of Energy Simulation Program “Energy Plus”. To be able to compare the Energy Conservation Measure case (Using Skylights) with the Base Line (No Skylights), a photometric sensor was modeled to ensure that both cases sourced the same amount of light visible in the electromagnetic spectrum. Considering the Heating, Cooling and lighting energy consumption as variables, the variance between the ECM and the Base line for the residential case was 5% more energy consumption with skylights. For the Big Box Commercial Building there was a 5% deduction in energy consumption in the ECM case using 5% roof area covered with skylights. The results obtained from this investigation reveal a very promising effect in the implementation of skylights in “Big Box Commercial Buildings”, but not so optimistic in the case of “Residential Buildings” for hot and humid climate as shown by the simulation and monitoring data from the experimental case.

The air conditioning (A/C) system is the largest ancillary load in passenger cars, with significant impact on fuel economy. In order to reduce the energy consumption of A/C systems, model-based optimization and optimal control design tools can be effectively applied to design of a supervisory energy management strategy. Significant challenges however lie in the design of a system model that is accurate enough to represent the nonlinear behavior of the system, yet sufficiently simple to enable the use of model-based control design methods. This paper presents a low-order, energy-based model of an automotive A/C system that is able to predict the dynamics of the evaporator and condenser pressures and the compressor power consumption during typical thermostatic (on/off) operations. A characterization of the mass and energy transport in the heat exchangers is obtained using a lumped-parameter approximation, leading to a model with reasonable accuracy but greatly reduced complexity, hence for supervisory control design. The model was validated against experimental data obtained on a test vehicle, allowing one to evaluate the accuracy in predicting the pressure states and the power consumption.

Many buildings around the world utilize HVAC systems to provide the thermal comfort conditions for the occupants and users of these buildings as well as providing adequate conditions for special environmentally sensitive equipment. The appropriate air conditioning system design would necessarily provide the comfort and hygiene conditions with as low as possible initial capital and operating costs. The present study provides comprehensive analyses and comparisons between the water-cooled vs. air-cooled chillers with packaged water-cooled DX unit systems under the local Egyptian climatic conditions, primary water and energy prices and legistilations. The present study presents a comparison among the various systems according to the equipment and installation, operating, and maintenance costs. Based on the above analyses and the data collected with the prevailing prices, energy, water and maintenance costs at January 2003 in Egypt. It is apparent that water-cooled packaged units are of superior performance in terms of energy consumption and initial costs.

The growing environmental footprint of data centers suggests the need for comprehensive models that enable designers and operators to optimize deployment configurations. In this paper, we discuss an exergy-based approach to the environmental design of one component of a data center, the computer room air conditioning (CRAC) unit. Previous work has examined the lifetime exergy consumption of the server technology portion of a data center. In this work we construct a lifetime exergy model for a direct expansion CRAC unit which when combined with the computer server model provides a more complete picture of the environmental impact of a data center. The lifetime model includes the material and energy consumption of the raw materials, transportation, manufacturing processes, operation and disposal. The application of the model is then explored under a variety of design configurations and utilization scenarios. Key parameters that affect the environmental footprint of the CRAC unit (as predicted by the lifetime exergy model) are identified. Case studies are performed to illustrate how variations in these key parameters can lead to significant differences in the lifetime exergy consumption of the CRAC unit. The paper concludes by recommending strategies to reduce the lifetime exergy consumption.

Abstract Rapid urbanization resulted in the drastic expansion of the built infrastructure in urban areas. This eventually led to an increase in energy consumption in the residential and commercial sectors. An appropriate selection of the convective heat transfer coefficient correlation at the design stage is of vital importance as it directly affects the cooling load of the building and consequently buildings’ energy demand. In this context, the comparative analysis of existing convective heat transfer coefficient correlations (CHTCs) used in building simulation programs such as EnergyPlus, ESP-r, IES, IDA, and TAS, are assessed. These correlations are tested against the data of Liu et al.’s [1]. It is observed that some of CHTCs correlations show lower error and others exhibit a significant deviation. In the case of the CHTCs used EnergyPlus, it is observed that the TARP algorithm shows overall better prediction ability for windward, leeward, and roof surface. On the other hand, in the case of the ESP-r, different correlations show a good prediction ability for different surfaces. For windward surface: MoWiTT; for leeward surface: MoWiTT and McAdams; and for roof surface: Liu and Harris show closer prediction with an error of less than 30% among other correlations. The correlation used in IES, IDA, and TAS shows a large deviation for windward, leeward, and roof surfaces under considered input. Based on this analysis, it can be concluded that the choice of such CHTCs uses in the BES tool can lead to the significantly higher energy consumption of the building and hence need the expertise to make the appropriate selection.