基于Elman神经网络的小水电集群发电能力预测OA

In response to the current situation of a large number of small hydropower stations in the watershed,scattered layouts,and a general lack of real-time operating data(black box characteristics),a data reconstruction and overall estimation framework based on"total amount back inference"was constructed by using the metering data of the dispatch center to reverse reconstruct the total electricity consumption of small hydropower stations.The Elman neural network with dynamic memory function was introduced to capture the nonlinear temporal coupling characteristics in the rainfall power generation process,forming a set of aggregated power generation capacity prediction methods for data scarcity scenarios.Taking the four typical river basins of Jinsha River,Yalong River,Dadu River and Minjiang River in Sichuan Province as examples for verification.The results show that the"cluster effect"of small hydropower can effectively smooth out random disturbances of single stations,making the model perform well in watersheds mainly supplied by rainwater(Minjiang River,Dadu River),such as Minjiang River Basin with coefficient of determination R2＞0.96 and mean absolute percentage error(MAPE)only 6.34％.However,in watersheds significantly replenished by snowmelt(Jinsha River,Yalong River),the applicability of a single rainfall driven model has certain limitations,indicating the necessity of introducing multi-source variables such as temperature under complex hydrological driving.The proposed aggregation modeling scheme effectively alleviates the data acquisition difficulties in poverty-stricken areas and provides key technical support for electricity market trading and settlement assessment.