In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） f...In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.展开更多
The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supe...The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.展开更多
To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the ...To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.展开更多
In this study,standards of high-biocontainment facilities(including laboratories and large-scale production facilities)formulated by China and other countries were analyzed and compared,and the technical points and re...In this study,standards of high-biocontainment facilities(including laboratories and large-scale production facilities)formulated by China and other countries were analyzed and compared,and the technical points and requirements for Heating,Ventilating,and Air Conditioning(HVAC)systems in different series of standards were discussed.The main focus was on expounding the design and verification of the containment area’s indoor parameters,ventilation system,filter design,integrity test,fan standby,airflow pattern,and system reliability.This study expects suggestions and opinions on the construction and development of biosafety facilities in China and the possible future revision of relevant national standards.展开更多
On average, long-haul trucks in the U.S. use approximately 667 million gallons of fuel each year just for idling. This idling primarily facilitates climate control operations during driver rest periods. To mitigate th...On average, long-haul trucks in the U.S. use approximately 667 million gallons of fuel each year just for idling. This idling primarily facilitates climate control operations during driver rest periods. To mitigate this, our study explored ways to diminish the electrical consumption of climate control systems in class 8 trucks through innovative load reduction technologies. We utilized the CoolCalc software, developed by the National Renewable Energy Laboratory (NREL), which integrates heat transfer principles with extensive weather data from across the U.S. to mimic the environmental conditions trucks face year-round. The analysis of the CoolCalc simulations was performed using MATLAB. We assessed the impact of various technologies, including white paint, advanced curtains, and Thinsulate insulation on reducing electrical demand compared to standard conditions. Our findings indicate that trucks operating in the eastern U.S. could see electrical load reductions of up to 40%, while those in the western regions could achieve reductions as high as 55%. Such significant decreases in energy consumption mean that a 10 kWh battery system could sufficiently manage the HVAC needs of these trucks throughout the year without idling. Given that many long-haul trucks are equipped with battery systems of around 800 Ah (9.6 kWh), implementing these advanced technologies could substantially curtail the necessity for idling to power air conditioning systems.展开更多
Large language models(LLMs)have shown human-level capabilities in solving various complex tasks.However,it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating,ventilation and ...Large language models(LLMs)have shown human-level capabilities in solving various complex tasks.However,it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating,ventilation and air conditioning(HVAC)systems.It will be inspiring if LLMs can think and learn like professionals in the HVAC industry.Hence,this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination,an authoritative examination in the HVAC industry.Three key knowledge capabilities are explored:recall,analysis and application.Twelve representative LLMs are tested such as GPT-3.5,GPT-4 and LLaMA.According to the results,GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78,which is higher than about half of human examinees.Besides,GPT-3.5 passes the examination twice out of five times.It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems.However,they still make some mistakes sometimes due to the lack of knowledge,poor reasoning capabilities and unsatisfactory equation calculation abilities.Accordingly,four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry:teaching LLMs to use design tools or software in the HVAC industry,enabling LLMs to read and analyze the operational data from HVAC systems,developing tailored corpuses for the HVAC industry,and assessing the performance of LLMs in real-world HVAC design and operation scenarios.展开更多
The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accurac...The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods,obtaining benchmarks for sensors during actual operation is challenging,making it difficult to diagnose the specific fault types of the sensors.To address this issue,an enhanced multi-sensor calibration(EMC)method has been developed to operate without prior knowledge of fault types.The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time.The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively.Furthermore,the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance.The proposed method has been validated on a chiller plant in a large public building in Hong Kong.The experimental results indicate that,under conditions involving eight sensor faults,including even three drift deviations,the EMC method achieved average calibration accurate rates of 100%for bias faults and 95%for drift faults.Notably,in calibrating drift faults,the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87%and 34%,respectively.展开更多
The on-going COVID-19 pandemic has wrecked havoc in our society,with short and long-term consequences to people’s lives and livelihoods-over 651 million COVID-19 cases have been confirmed with the number of deaths ex...The on-going COVID-19 pandemic has wrecked havoc in our society,with short and long-term consequences to people’s lives and livelihoods-over 651 million COVID-19 cases have been confirmed with the number of deaths exceeding 6.66 million.As people stay indoors most of the time,how to operate the Heating,Ventilation and Air-Conditioning(HVAC)systems as well as building facilities to reduce airborne infections have become hot research topics.This paper presents a systematic review on COVID-19 related research in HVAC systems and the indoor environment.Firstly,it reviews the research on the improvement of ventilation,filtration,heating and air-conditioning systems since the onset of COVID-19.Secondly,various indoor environment improvement measures to minimize airborne spread,such as building envelope design,physical barriers and vent position arrangement,and the possible impact of COVID-19 on building energy consumption are examined.Thirdly,it provides comparisons on the building operation guidelines for preventing the spread of COVID-19 virus from different countries.Finally,recommendations for future studies are provided.展开更多
Heating,ventilation,and air conditioning(HVAC)systems consume a significant amount of energy to maintain thermal comfort and indoor air quality in buildings,which results in high operational costs.Reinforcement learni...Heating,ventilation,and air conditioning(HVAC)systems consume a significant amount of energy to maintain thermal comfort and indoor air quality in buildings,which results in high operational costs.Reinforcement learning is an effective method for controlling HVAC systems.However,in large and complex HVAC systems,traditional reinforcement learning algorithms often face the challenges of slow training speed and poor convergence performance.This paper proposes a multi-objective optimization control method based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,which aims to minimize HVAC energy consumption while ensuring optimal thermal comfort and indoor air quality in each zone.Using a multi-zone office building with fan coil units and a dedicated outdoor air system as a case study,we developed an EnergyPlus-Python co-simulation platform.The proposed control method was employed during both the heating and cooling seasons to independently control the temperature setpoints and fresh airflow in different zones of the office building.The simulation results from both the heating and cooling seasons demonstrate that the MADDPG control method exhibits faster convergence during training and excellent learning capabilities,allowing it to adapt effectively to changes in environmental conditions and implement appropriate control actions.Under similar indoor thermal comfort and air quality conditions,the MADDPG control method consumes less energy than the traditional reinforcement learning method,it saves 24.1%of energy during the heating season and 8.9%during the cooling season compared to the rule-based control method.Additionally,by adjusting the reward function in the MADDPG algorithm,it is possible to flexibly balance energy consumption,thermal comfort,and air quality preferences,demonstrating the algorithm’s strong applicability.展开更多
Air distribution of HVAC systems is the most popular type used in the building sector,having a relevant impact on indoor air quality and occupant wellness.Many types of research developed optimal solutions for the HVA...Air distribution of HVAC systems is the most popular type used in the building sector,having a relevant impact on indoor air quality and occupant wellness.Many types of research developed optimal solutions for the HVAC system’s design,focusing on specific components of the distribution system,on the airflow and geometry of ducts,on the size of ducts,on the shape and position of air diffusers.However,few works in literature proposed a globally experimental and simulation analysis of an air distribution system with a variable mass flaw rate.Along this line,the presented research investigates the potentialities of a new ceiling diffuser,installed in an exhibition room.This system provides a variable mass flow rate thanks to its configuration,providing adequate thermal comfort.A warm wall is chosen as the heating system.Several tests are carried out,six for cooling and two for heating with different volumetric air rates and supply air temperature of the diffusers.The combination of two methods,the measurement campaigns and the computational fluid dynamics(CFD)technique represent a suitable approach to examine the thermal indoor environment.In general,results show a strong capability of this diffuser to provide a uniform temperature and velocity field inside the room.Moreover,experimental and numerical data are significantly comparable with an average deviation of 1%for the velocity and lower 1%for the temperature,guaranteeing an optimal distribution of the understudy environmental parameters on the vertical and horizontal planes.展开更多
The energy-savings of four hypothetical households in different climatic regions of Turkey were calculated via a nonlinear mixed integer optimization model.The ideal insulation material,its optimum thickness,and the i...The energy-savings of four hypothetical households in different climatic regions of Turkey were calculated via a nonlinear mixed integer optimization model.The ideal insulation material,its optimum thickness,and the ideal window type were determined.The standard degree days method was used with five different base temperatures for heating and five different base temperatures for cooling.The climatic conditions of the region,the properties of the insulation options,the unit price of fuel and electricity and the base temperature are used as model inputs,whereas the combination of selected insulation material with its optimum thickness and window type are given as model outputs.Stone Wool was found to be the ideal wall insulation material in all scenarios.The optimum window type was found to depend on the heating or cooling requirements of the house,as well as the lifetime of insulation.The region where the energy saving actions are deemed most feasible has been identified as Erzurum(Region 4),followed by Antalya(Region 1).Finally,the effect of changing the base temperature on energy savings was investigated and the results showed that an approximate average increase of$15/℃ in annual savings is possible.Our model can be used by any prospective home-owner who would like to maximize their energy savings.展开更多
基金This work was supported by Young Scientists Fundamental Research Program of Shandong Province of China (No. 031B5147).
文摘In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.
基金support of this research by the National Natural Science Foundation of China (No.52278117)the Philosophical and Social Science Program of Guangdong Province,China (GD22XGL20)the Shenzhen Science and Technology Program (No.20220531101800001 and No.20220810160221001).
文摘The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.
基金National Key R&D Program of China of the 13th Five-Year Plan(No.2018YFD1100704)。
文摘To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.
文摘In this study,standards of high-biocontainment facilities(including laboratories and large-scale production facilities)formulated by China and other countries were analyzed and compared,and the technical points and requirements for Heating,Ventilating,and Air Conditioning(HVAC)systems in different series of standards were discussed.The main focus was on expounding the design and verification of the containment area’s indoor parameters,ventilation system,filter design,integrity test,fan standby,airflow pattern,and system reliability.This study expects suggestions and opinions on the construction and development of biosafety facilities in China and the possible future revision of relevant national standards.
文摘On average, long-haul trucks in the U.S. use approximately 667 million gallons of fuel each year just for idling. This idling primarily facilitates climate control operations during driver rest periods. To mitigate this, our study explored ways to diminish the electrical consumption of climate control systems in class 8 trucks through innovative load reduction technologies. We utilized the CoolCalc software, developed by the National Renewable Energy Laboratory (NREL), which integrates heat transfer principles with extensive weather data from across the U.S. to mimic the environmental conditions trucks face year-round. The analysis of the CoolCalc simulations was performed using MATLAB. We assessed the impact of various technologies, including white paint, advanced curtains, and Thinsulate insulation on reducing electrical demand compared to standard conditions. Our findings indicate that trucks operating in the eastern U.S. could see electrical load reductions of up to 40%, while those in the western regions could achieve reductions as high as 55%. Such significant decreases in energy consumption mean that a 10 kWh battery system could sufficiently manage the HVAC needs of these trucks throughout the year without idling. Given that many long-haul trucks are equipped with battery systems of around 800 Ah (9.6 kWh), implementing these advanced technologies could substantially curtail the necessity for idling to power air conditioning systems.
基金supported by the National Natural Science Founda-tion of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028)the Basic Research Funds for the Central Government‘Innovative Team of Zhejiang University’(No.2022FZZX01-09),and China Scholarship Fund.
文摘Large language models(LLMs)have shown human-level capabilities in solving various complex tasks.However,it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating,ventilation and air conditioning(HVAC)systems.It will be inspiring if LLMs can think and learn like professionals in the HVAC industry.Hence,this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination,an authoritative examination in the HVAC industry.Three key knowledge capabilities are explored:recall,analysis and application.Twelve representative LLMs are tested such as GPT-3.5,GPT-4 and LLaMA.According to the results,GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78,which is higher than about half of human examinees.Besides,GPT-3.5 passes the examination twice out of five times.It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems.However,they still make some mistakes sometimes due to the lack of knowledge,poor reasoning capabilities and unsatisfactory equation calculation abilities.Accordingly,four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry:teaching LLMs to use design tools or software in the HVAC industry,enabling LLMs to read and analyze the operational data from HVAC systems,developing tailored corpuses for the HVAC industry,and assessing the performance of LLMs in real-world HVAC design and operation scenarios.
基金the Natural Science Foundation of Jiangsu Province(BK20240560)the China Postdoctoral Science Foundation(2024M764219)+1 种基金the Science and Technology Project of Jiangsu Provincial Department of Housing and Urban Rural Development(2023ZD026)the Science and Technology Project of Nanjing Municipal Commission of Urban and Rural Development(Ks2415).
文摘The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods,obtaining benchmarks for sensors during actual operation is challenging,making it difficult to diagnose the specific fault types of the sensors.To address this issue,an enhanced multi-sensor calibration(EMC)method has been developed to operate without prior knowledge of fault types.The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time.The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively.Furthermore,the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance.The proposed method has been validated on a chiller plant in a large public building in Hong Kong.The experimental results indicate that,under conditions involving eight sensor faults,including even three drift deviations,the EMC method achieved average calibration accurate rates of 100%for bias faults and 95%for drift faults.Notably,in calibrating drift faults,the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87%and 34%,respectively.
文摘The on-going COVID-19 pandemic has wrecked havoc in our society,with short and long-term consequences to people’s lives and livelihoods-over 651 million COVID-19 cases have been confirmed with the number of deaths exceeding 6.66 million.As people stay indoors most of the time,how to operate the Heating,Ventilation and Air-Conditioning(HVAC)systems as well as building facilities to reduce airborne infections have become hot research topics.This paper presents a systematic review on COVID-19 related research in HVAC systems and the indoor environment.Firstly,it reviews the research on the improvement of ventilation,filtration,heating and air-conditioning systems since the onset of COVID-19.Secondly,various indoor environment improvement measures to minimize airborne spread,such as building envelope design,physical barriers and vent position arrangement,and the possible impact of COVID-19 on building energy consumption are examined.Thirdly,it provides comparisons on the building operation guidelines for preventing the spread of COVID-19 virus from different countries.Finally,recommendations for future studies are provided.
基金this study was sponsored by the National Natural Science Foundation of China(Grant Number:52278103)the Natural Science Foundation-Departmental Joint Fund of Hunan Province,China(Grant Number:2023JJ60570).
文摘Heating,ventilation,and air conditioning(HVAC)systems consume a significant amount of energy to maintain thermal comfort and indoor air quality in buildings,which results in high operational costs.Reinforcement learning is an effective method for controlling HVAC systems.However,in large and complex HVAC systems,traditional reinforcement learning algorithms often face the challenges of slow training speed and poor convergence performance.This paper proposes a multi-objective optimization control method based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,which aims to minimize HVAC energy consumption while ensuring optimal thermal comfort and indoor air quality in each zone.Using a multi-zone office building with fan coil units and a dedicated outdoor air system as a case study,we developed an EnergyPlus-Python co-simulation platform.The proposed control method was employed during both the heating and cooling seasons to independently control the temperature setpoints and fresh airflow in different zones of the office building.The simulation results from both the heating and cooling seasons demonstrate that the MADDPG control method exhibits faster convergence during training and excellent learning capabilities,allowing it to adapt effectively to changes in environmental conditions and implement appropriate control actions.Under similar indoor thermal comfort and air quality conditions,the MADDPG control method consumes less energy than the traditional reinforcement learning method,it saves 24.1%of energy during the heating season and 8.9%during the cooling season compared to the rule-based control method.Additionally,by adjusting the reward function in the MADDPG algorithm,it is possible to flexibly balance energy consumption,thermal comfort,and air quality preferences,demonstrating the algorithm’s strong applicability.
文摘Air distribution of HVAC systems is the most popular type used in the building sector,having a relevant impact on indoor air quality and occupant wellness.Many types of research developed optimal solutions for the HVAC system’s design,focusing on specific components of the distribution system,on the airflow and geometry of ducts,on the size of ducts,on the shape and position of air diffusers.However,few works in literature proposed a globally experimental and simulation analysis of an air distribution system with a variable mass flaw rate.Along this line,the presented research investigates the potentialities of a new ceiling diffuser,installed in an exhibition room.This system provides a variable mass flow rate thanks to its configuration,providing adequate thermal comfort.A warm wall is chosen as the heating system.Several tests are carried out,six for cooling and two for heating with different volumetric air rates and supply air temperature of the diffusers.The combination of two methods,the measurement campaigns and the computational fluid dynamics(CFD)technique represent a suitable approach to examine the thermal indoor environment.In general,results show a strong capability of this diffuser to provide a uniform temperature and velocity field inside the room.Moreover,experimental and numerical data are significantly comparable with an average deviation of 1%for the velocity and lower 1%for the temperature,guaranteeing an optimal distribution of the understudy environmental parameters on the vertical and horizontal planes.
文摘The energy-savings of four hypothetical households in different climatic regions of Turkey were calculated via a nonlinear mixed integer optimization model.The ideal insulation material,its optimum thickness,and the ideal window type were determined.The standard degree days method was used with five different base temperatures for heating and five different base temperatures for cooling.The climatic conditions of the region,the properties of the insulation options,the unit price of fuel and electricity and the base temperature are used as model inputs,whereas the combination of selected insulation material with its optimum thickness and window type are given as model outputs.Stone Wool was found to be the ideal wall insulation material in all scenarios.The optimum window type was found to depend on the heating or cooling requirements of the house,as well as the lifetime of insulation.The region where the energy saving actions are deemed most feasible has been identified as Erzurum(Region 4),followed by Antalya(Region 1).Finally,the effect of changing the base temperature on energy savings was investigated and the results showed that an approximate average increase of$15/℃ in annual savings is possible.Our model can be used by any prospective home-owner who would like to maximize their energy savings.
