China is actively developing passive houses to improve energy efficiency and reduce primary energy use.These buildings have low actual load characteristics,resulting in a smaller air conditioning terminal heating capa...China is actively developing passive houses to improve energy efficiency and reduce primary energy use.These buildings have low actual load characteristics,resulting in a smaller air conditioning terminal heating capacity.However,owing to this limited heating capacity,the air conditioning terminal provides modest indoor air regulation.In practice,occupants of passive houses often open windows for ventilation in winter,resulting in an indoor temperature that is lower than the set value,and it takes a long time for the temperature to return to the desired level.There are few studies that have investigated this issue.Two possible solutions to this issue are proposed:first,altering the external wall structure can enhance the thermal response rate,reducing the time needed for temperature recovery.Secondly,limiting the window-opening area can minimize heat loss during ventilation,thereby maintaining a reasonable indoor temperature.Notably,this may inconvenience occupants.Taking a passive house in Qinghai as a case study,this study discusses the influence of window-opening behavior on the indoor thermal environment.Simulations were conducted to study the window-opening behavior considering different window-opening areas and wall structures.Three different wall structures were considered:internal insulation combined with external insulation(IAE),sandwich insulation combined with external insulation(SAE),and external insulation structures.The results indicate that the IAE and SAE structures cannot effectively improve the indoor thermal environment after opening a window.Overall,changing the wall structure does not reduce the time required to restore room temperature.However,by limiting the opening area of the window,the room temperature can be effectively controlled.Under the given window opening ratio,the room temperature can be stabilized above 18◦C.This study offers a practical method for controlling and enhancing the indoor thermal environment,which is applicable to the construction and development of passive houses.展开更多
As distributed energy systems become increasingly prevalent,residential energy systems(RES)equipped with photovoltaics(PV)face significant challenges in maintaining supply-demand balance due to power output fluctuatio...As distributed energy systems become increasingly prevalent,residential energy systems(RES)equipped with photovoltaics(PV)face significant challenges in maintaining supply-demand balance due to power output fluctuations.This necessitates short-term PV power prediction methods that effectively balance accuracy and deployment cost.To address this issue,this paper proposes a novel short-term PV power prediction approach based on low-cost ground-based sky image sequences:the 3DCNN-DLinear model.The method leverages fisheye camera-captured sky images to extract spatiotemporal features via a three-dimensional convolutional neural network(3DCNN),and integrates a lightweight time-series model,DLinear,to enable efficient prediction.The proposed model was evaluated using real-world data collected in Changping District,Beijing,China.A comparative analysis involving six mainstream time-series models confirmed that DLinear achieved the lowest overall prediction error.Further experiments demonstrated that the 3DCNN-DLinear model reduced RMSE by 49.28%,9.56%,and 8.82%for 30-,60-,and 90-minute prediction tasks,respectively,compared to the baseline 3DCNN-LSTM model.Additionally,the study examined the contribution of sky image data to prediction accuracy,revealing significant improvements under varying conditions.Notably,RMSE was reduced by 40.4%and 30.5%under sunny and cloudy conditions,respectively,for the 60-minute task.Overall,the proposed method offers an effective and economically viable solution to improve the predictive performance and intelligent scheduling of RES.展开更多
基金supported by the Local Science and Technology Development Fund Project:Research on Low Carbon Transformation Methods for Residen-tial Buildings(XZ202301YD0009C).
文摘China is actively developing passive houses to improve energy efficiency and reduce primary energy use.These buildings have low actual load characteristics,resulting in a smaller air conditioning terminal heating capacity.However,owing to this limited heating capacity,the air conditioning terminal provides modest indoor air regulation.In practice,occupants of passive houses often open windows for ventilation in winter,resulting in an indoor temperature that is lower than the set value,and it takes a long time for the temperature to return to the desired level.There are few studies that have investigated this issue.Two possible solutions to this issue are proposed:first,altering the external wall structure can enhance the thermal response rate,reducing the time needed for temperature recovery.Secondly,limiting the window-opening area can minimize heat loss during ventilation,thereby maintaining a reasonable indoor temperature.Notably,this may inconvenience occupants.Taking a passive house in Qinghai as a case study,this study discusses the influence of window-opening behavior on the indoor thermal environment.Simulations were conducted to study the window-opening behavior considering different window-opening areas and wall structures.Three different wall structures were considered:internal insulation combined with external insulation(IAE),sandwich insulation combined with external insulation(SAE),and external insulation structures.The results indicate that the IAE and SAE structures cannot effectively improve the indoor thermal environment after opening a window.Overall,changing the wall structure does not reduce the time required to restore room temperature.However,by limiting the opening area of the window,the room temperature can be effectively controlled.Under the given window opening ratio,the room temperature can be stabilized above 18◦C.This study offers a practical method for controlling and enhancing the indoor thermal environment,which is applicable to the construction and development of passive houses.
基金supported by the Energy System Low-Carbon Retrofit and Commissioning&Operation Technology project,which was supported by Beijing Uni-Constrction Group Co.,LTD.
文摘As distributed energy systems become increasingly prevalent,residential energy systems(RES)equipped with photovoltaics(PV)face significant challenges in maintaining supply-demand balance due to power output fluctuations.This necessitates short-term PV power prediction methods that effectively balance accuracy and deployment cost.To address this issue,this paper proposes a novel short-term PV power prediction approach based on low-cost ground-based sky image sequences:the 3DCNN-DLinear model.The method leverages fisheye camera-captured sky images to extract spatiotemporal features via a three-dimensional convolutional neural network(3DCNN),and integrates a lightweight time-series model,DLinear,to enable efficient prediction.The proposed model was evaluated using real-world data collected in Changping District,Beijing,China.A comparative analysis involving six mainstream time-series models confirmed that DLinear achieved the lowest overall prediction error.Further experiments demonstrated that the 3DCNN-DLinear model reduced RMSE by 49.28%,9.56%,and 8.82%for 30-,60-,and 90-minute prediction tasks,respectively,compared to the baseline 3DCNN-LSTM model.Additionally,the study examined the contribution of sky image data to prediction accuracy,revealing significant improvements under varying conditions.Notably,RMSE was reduced by 40.4%and 30.5%under sunny and cloudy conditions,respectively,for the 60-minute task.Overall,the proposed method offers an effective and economically viable solution to improve the predictive performance and intelligent scheduling of RES.
