To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to t...To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to the scarcity of smart meters installed,it is challenging to obtain such information via directly aggregating measured power outputs of individual PV systems,and an effective estimation method needs to be developed.Considering the similarity between household-level and community-level data within the same geographical area,this paper develops a synchronization and model-transfer fused LSTM framework(SAM-LSTM).The core technical contribution lies in the development of the Synchronized Long Short-Term Memory(Syn-LSTM),which separately models the synchronized factors and disaggregated BTM data to capture more generalized representations.The learned household-level representations are then transferred to the community-level.Finally,by explicitly leveraging the complementarity between PV generation and consumption,a dual time-series modeling architecture is developed to refine the initial community-level PV power generation estimates,thereby alleviating potential biases introduced during the model-transfer process.Extensive computational studies are conducted to demonstrate the effectiveness of SAM-LSTM in community-level BTM PV power generation disaggregation in real data from Hebei,China.Compared with the best-performing benchmarks,SAM-LSTM achieves up to 56%lower MSE,significantly demonstrating its strong generalization and robustness capabilities.展开更多
基金National Natural Science Foun-dation of China under Grant(No.62572116)Hong Kong RGC General Research Fund Project under Grant(No.11213124)+5 种基金Guangdong Provincial Basic and Applied Basic Re-search-Offshore Wind Power Joint Fund Project under Grant(No.2022A1515240066)Hong Kong RGC Collaborative Research Fund Project under Grant(No.C1049-24GF)Shenzhen-Hong Kong-Macao Science and Technology Category C Project under Grant(No.SGDX20220530111205037)Hong Kong ITC Innovation and Technology Fund Project under Grant(No.ITS/034/22MS)Young Elite Scientists Sponsorship Program by CAST under Grant(No.2023ONRC001)National Natural Science Foundation of China under Grant(No.62576098).
文摘To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to the scarcity of smart meters installed,it is challenging to obtain such information via directly aggregating measured power outputs of individual PV systems,and an effective estimation method needs to be developed.Considering the similarity between household-level and community-level data within the same geographical area,this paper develops a synchronization and model-transfer fused LSTM framework(SAM-LSTM).The core technical contribution lies in the development of the Synchronized Long Short-Term Memory(Syn-LSTM),which separately models the synchronized factors and disaggregated BTM data to capture more generalized representations.The learned household-level representations are then transferred to the community-level.Finally,by explicitly leveraging the complementarity between PV generation and consumption,a dual time-series modeling architecture is developed to refine the initial community-level PV power generation estimates,thereby alleviating potential biases introduced during the model-transfer process.Extensive computational studies are conducted to demonstrate the effectiveness of SAM-LSTM in community-level BTM PV power generation disaggregation in real data from Hebei,China.Compared with the best-performing benchmarks,SAM-LSTM achieves up to 56%lower MSE,significantly demonstrating its strong generalization and robustness capabilities.
