As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time o...As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.展开更多
The indoor parameters are generally non-uniform distributed.Consequently,it is important to study the space cooling/heating load oriented to local requirements.Though the influence of indoor set point,heat sources,and...The indoor parameters are generally non-uniform distributed.Consequently,it is important to study the space cooling/heating load oriented to local requirements.Though the influence of indoor set point,heat sources,and ambient temperature of convective thermal boundary on cooling/heating load has been investigated in the uniform environment in previous research,the influence of these factors,particularly the convective heat gain/loss through a building envelope,on cooling/heating load of non-uniform environment has not yet been investigated.Therefore,based on the explicit expression of indoor temperature under the convective boundary condition,the expression of space cooling/heating load with convective heat transfer from the building envelope is derived and compared through case studies.The results can be summarized as follows.(1)The convective heat transferred through the building envelope is significantly related to the airflow patterns:the heating load in the case with ceiling supply air,where the supply air has a smaller contribution to the local zone,is 24%higher than that in the case with bottom supply air.(2)The degree of influence from each thermal boundary to the local zone of space cooling cases is close to that of a uniform environment,while the influence of each factor,particularly that of supply air,is non-uniformly distributed in space heating.(3)It is possible to enhance the influence of supply air and heat source with a reasonable airflow pattern to reduce the space heating load.In general,the findings of this study can be used to guide the energy savings of rooms with non-uniform environments for space cooling/heating.展开更多
The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to be...The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.展开更多
To predict the thermal and structural responses of the thrust chamber wall under cyclic work,a 3-D fluid-structural coupling computational methodology is developed.The thermal and mechanical loads are determined by a ...To predict the thermal and structural responses of the thrust chamber wall under cyclic work,a 3-D fluid-structural coupling computational methodology is developed.The thermal and mechanical loads are determined by a validated 3-D finite volume fluid-thermal coupling computational method.With the specified loads,the nonlinear thermal-structural finite element analysis is applied to obtaining the 3-D thermal and structural responses.The Chaboche nonlinear kinematic hardening model calibrated by experimental data is adopted to predict the cyclic plastic behavior of the inner wall.The methodology is further applied to the thrust chamber of LOX/Methane rocket engines.The results show that both the maximum temperature at hot run phase and the maximum circumferential residual strain of the inner wall appear at the convergent part of the chamber.Structural analysis for multiple work cycles reveals that the failure of the inner wall may be controlled by the low-cycle fatigue when the Chaboche model parameter c3= 0,and the damage caused by the thermal-mechanical ratcheting of the inner wall cannot be ignored when c3〉 0.The results of sensitivity analysis indicate that mechanical loads have a strong influence on the strains in the inner wall.展开更多
Wind-driven rain(WDR)has a significant influence on the hygrothermal performance,durability,and energy consumption of building components.The calculation of WDR loads using semi-empirical models has been incorporated ...Wind-driven rain(WDR)has a significant influence on the hygrothermal performance,durability,and energy consumption of building components.The calculation of WDR loads using semi-empirical models has been incorporated into the boundary conditions of coupled heat and moisture transfer models.However,prior research often relied on fixed WDR absorption ratio,which fail to accurately capture the water absorption characteristics of porous building materials under rainfall scenarios.Therefore,this study aims to investigate the coupled heat and moisture transfer of exterior walls under dynamic WDR boundary conditions,utilizing an empirically obtained WDR absorption ratio model based on field measurements.The developed coupled heat and moisture transfer model is validated against the HAMSTAD project.The findings reveal that the total WDR flux calculated with the dynamic WDR boundary is lower than that obtained with the fixed WDR boundary,with greater disparities observed in orientations experiencing higher WDR loads.The variations in moisture flow significantly impact the surface temperature and relative humidity of the walls,influencing the calculation of cooling and heating loads by different models.Compared to the transient heat transfer model,the coupled heat and moisture transfer model incorporating dynamic WDR boundary exhibits maximum increases of 17.6%and 16.2%in cooling and heating loads,respectively.The dynamic WDR boundary conditions provide more precise numerical values for surface moisture flux,offering valuable insights for the thermal design of building enclosures and load calculations for HVAC systems.展开更多
基金This project was supported by National Key Research and Development Plan(2017YFB0902100)Key Project of Liaoning Natural Science Foundation under Grant(20170520292).
文摘As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.
基金supported by the National Natural Science Foundation of China(No.51638010 and No.51578306).
文摘The indoor parameters are generally non-uniform distributed.Consequently,it is important to study the space cooling/heating load oriented to local requirements.Though the influence of indoor set point,heat sources,and ambient temperature of convective thermal boundary on cooling/heating load has been investigated in the uniform environment in previous research,the influence of these factors,particularly the convective heat gain/loss through a building envelope,on cooling/heating load of non-uniform environment has not yet been investigated.Therefore,based on the explicit expression of indoor temperature under the convective boundary condition,the expression of space cooling/heating load with convective heat transfer from the building envelope is derived and compared through case studies.The results can be summarized as follows.(1)The convective heat transferred through the building envelope is significantly related to the airflow patterns:the heating load in the case with ceiling supply air,where the supply air has a smaller contribution to the local zone,is 24%higher than that in the case with bottom supply air.(2)The degree of influence from each thermal boundary to the local zone of space cooling cases is close to that of a uniform environment,while the influence of each factor,particularly that of supply air,is non-uniformly distributed in space heating.(3)It is possible to enhance the influence of supply air and heat source with a reasonable airflow pattern to reduce the space heating load.In general,the findings of this study can be used to guide the energy savings of rooms with non-uniform environments for space cooling/heating.
文摘The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.
文摘To predict the thermal and structural responses of the thrust chamber wall under cyclic work,a 3-D fluid-structural coupling computational methodology is developed.The thermal and mechanical loads are determined by a validated 3-D finite volume fluid-thermal coupling computational method.With the specified loads,the nonlinear thermal-structural finite element analysis is applied to obtaining the 3-D thermal and structural responses.The Chaboche nonlinear kinematic hardening model calibrated by experimental data is adopted to predict the cyclic plastic behavior of the inner wall.The methodology is further applied to the thrust chamber of LOX/Methane rocket engines.The results show that both the maximum temperature at hot run phase and the maximum circumferential residual strain of the inner wall appear at the convergent part of the chamber.Structural analysis for multiple work cycles reveals that the failure of the inner wall may be controlled by the low-cycle fatigue when the Chaboche model parameter c3= 0,and the damage caused by the thermal-mechanical ratcheting of the inner wall cannot be ignored when c3〉 0.The results of sensitivity analysis indicate that mechanical loads have a strong influence on the strains in the inner wall.
基金The work described in this paper was financially supported by the Shanghai Municipality Natural Science Foundation(No.21ZR1434400).
文摘Wind-driven rain(WDR)has a significant influence on the hygrothermal performance,durability,and energy consumption of building components.The calculation of WDR loads using semi-empirical models has been incorporated into the boundary conditions of coupled heat and moisture transfer models.However,prior research often relied on fixed WDR absorption ratio,which fail to accurately capture the water absorption characteristics of porous building materials under rainfall scenarios.Therefore,this study aims to investigate the coupled heat and moisture transfer of exterior walls under dynamic WDR boundary conditions,utilizing an empirically obtained WDR absorption ratio model based on field measurements.The developed coupled heat and moisture transfer model is validated against the HAMSTAD project.The findings reveal that the total WDR flux calculated with the dynamic WDR boundary is lower than that obtained with the fixed WDR boundary,with greater disparities observed in orientations experiencing higher WDR loads.The variations in moisture flow significantly impact the surface temperature and relative humidity of the walls,influencing the calculation of cooling and heating loads by different models.Compared to the transient heat transfer model,the coupled heat and moisture transfer model incorporating dynamic WDR boundary exhibits maximum increases of 17.6%and 16.2%in cooling and heating loads,respectively.The dynamic WDR boundary conditions provide more precise numerical values for surface moisture flux,offering valuable insights for the thermal design of building enclosures and load calculations for HVAC systems.
