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
2024年HVAC(Heating,Ventilation and Air Conditioning,空气调节系统)现场设备市场规模预计为211.3亿美元,预计到2029年将达到287.2亿美元,在预测期内(2024-2029年)复合年增长率为6.3%。新冠肺炎疫情对暖通空调行业产生了重大影响,由...2024年HVAC(Heating,Ventilation and Air Conditioning,空气调节系统)现场设备市场规模预计为211.3亿美元,预计到2029年将达到287.2亿美元,在预测期内(2024-2029年)复合年增长率为6.3%。新冠肺炎疫情对暖通空调行业产生了重大影响,由于封锁限制和企业避免投资新设备,全球许多建设项目被迫暂停。展开更多
Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC)...Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were optimized.Moreover,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in TRNSYS.The results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.展开更多
The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep r...The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep reinforcement learning(DRL)methods have recently been proposed to address the HVAC control problem.However,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow convergence.In this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal comfort.MAQMC is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort violation.In addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.展开更多
We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zo...We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown dynamics.It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning algorithm.We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather changes.To address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control parameters.Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.展开更多
In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration...In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.展开更多
This research evaluates the performance of a Phase Change Material(PCM)battery integrated into the climate system of a new transparent meeting center.The main research questions are:a.“Can the performance of the batt...This research evaluates the performance of a Phase Change Material(PCM)battery integrated into the climate system of a new transparent meeting center.The main research questions are:a.“Can the performance of the battery be calculated?”and b.“Can the battery reduce the heating and cooling energy demand in a significant way?”The first question is answered in this document.In order to be able to answer the second question,especially the way the heat loading in winter should be improved,then more research is necessary.In addition to the thermal battery,which consists of Phase Change Material plates,the climate system has a cross-flow heat exchanger and a heat pump.The battery should play a central role in closing the thermal balance of the lightweight building,which can be loaded with hot return or cold outdoor air.The temperature of the battery plates is monitored by multi-sensors and simulated by the use of PHOENICS(Computational Fluid Dynamics)and MATLAB.This paper reports reasonable agreement between the numerical predictions and the measurements,with a maximum variance of 10%.The current coefficient of performance for heating and cooling is already high,more than 27.There is scope for increasing this much further by making use of the very low-pressure difference of the battery(below 25 Pascal),low pressure fans and the ventilation system as a whole.展开更多
Volume 292,1 August 2023 https://www.sciencedirect.com/journal/energy-and-buildings/vol/292/suppl/C.【OA】(1)Thermal modeling of existing buildings in high-fi-d elity simulators:A novel,practical methodology,by J.A.B ...Volume 292,1 August 2023 https://www.sciencedirect.com/journal/energy-and-buildings/vol/292/suppl/C.【OA】(1)Thermal modeling of existing buildings in high-fi-d elity simulators:A novel,practical methodology,by J.A.B orja-Conde,K.Witheephanich,J.F.Coronel,D.Limon,Arti-c le113127.Abstract:Optimizing efficiency in the operation of the HVACs ystem of existing buildings requires the construction of a thermal dynamic model of the building,which may be challenging becausea rchitectural metadata may be missing or obsolete.展开更多
As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Ge...As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.展开更多
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.
文摘2024年HVAC(Heating,Ventilation and Air Conditioning,空气调节系统)现场设备市场规模预计为211.3亿美元,预计到2029年将达到287.2亿美元,在预测期内(2024-2029年)复合年增长率为6.3%。新冠肺炎疫情对暖通空调行业产生了重大影响,由于封锁限制和企业避免投资新设备,全球许多建设项目被迫暂停。
基金supported by National Key R&D Program of China(No.2020YFC2006602)National Natural Science Foundation of China(Nos.62072324,61876217,61876121,61772357)+1 种基金University Natural Science Foundation of Jiangsu Province(No.21KJA520005)Primary Research and Development Plan of Jiangsu Province(No.BE2020026).
文摘Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were optimized.Moreover,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in TRNSYS.The results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.
基金supported by Primary Research and Development Plan of China(No.2020YFC2006602)National Natural Science Foundation of China(Nos.62072324,61876217,61876121,61772357)+2 种基金University Natural Science Foundation of Jiangsu Province(No.21KJA520005)Primary Research and Development Plan of Jiangsu Province(No.BE2020026)Natural Science Foundation of Jiangsu Province(No.BK20190942).
文摘The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep reinforcement learning(DRL)methods have recently been proposed to address the HVAC control problem.However,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow convergence.In this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal comfort.MAQMC is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort violation.In addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.
文摘We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown dynamics.It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning algorithm.We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather changes.To address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control parameters.Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.
文摘In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.
基金The project is financed via a public grant of the Rijksdienst Voor Ondernemend Nederland(RVO,https://www.rvo.nl/)within the Urban Energy 2018 Research-Line with Grant No.TEUE318008.The grant is awarded to the following consortium:The Delft University of Technology(https://www.tudelft.nl/en),Van Dorp B.V.,Hunter Douglas Europe B.V.(https://www.hunterdouglasarchitectural.eu/),Priva B.V.(https://www.priva.com/nl)and the Green Village Foundation(https://www.thegreenvillage.org/en/).
文摘This research evaluates the performance of a Phase Change Material(PCM)battery integrated into the climate system of a new transparent meeting center.The main research questions are:a.“Can the performance of the battery be calculated?”and b.“Can the battery reduce the heating and cooling energy demand in a significant way?”The first question is answered in this document.In order to be able to answer the second question,especially the way the heat loading in winter should be improved,then more research is necessary.In addition to the thermal battery,which consists of Phase Change Material plates,the climate system has a cross-flow heat exchanger and a heat pump.The battery should play a central role in closing the thermal balance of the lightweight building,which can be loaded with hot return or cold outdoor air.The temperature of the battery plates is monitored by multi-sensors and simulated by the use of PHOENICS(Computational Fluid Dynamics)and MATLAB.This paper reports reasonable agreement between the numerical predictions and the measurements,with a maximum variance of 10%.The current coefficient of performance for heating and cooling is already high,more than 27.There is scope for increasing this much further by making use of the very low-pressure difference of the battery(below 25 Pascal),low pressure fans and the ventilation system as a whole.
文摘Volume 292,1 August 2023 https://www.sciencedirect.com/journal/energy-and-buildings/vol/292/suppl/C.【OA】(1)Thermal modeling of existing buildings in high-fi-d elity simulators:A novel,practical methodology,by J.A.B orja-Conde,K.Witheephanich,J.F.Coronel,D.Limon,Arti-c le113127.Abstract:Optimizing efficiency in the operation of the HVACs ystem of existing buildings requires the construction of a thermal dynamic model of the building,which may be challenging becausea rchitectural metadata may be missing or obsolete.
文摘As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.
