考虑时空相关性的水风光互补系统发电功率联合预测OA

[Objective]Existing prediction methods struggle to accurately characterize the complex spatiotemporal complementarity of multi-source hydro-wind-photovoltaic series in large-scale new energy bases,thereby limiting prediction accuracy.To address this issue,this paper proposes a spatiotemporal joint prediction method based on bi-level complementarity regularization.[Methods]First,a seasonal trend decomposition spatial-temporal Transformer(STD-ST-Former)is constructed,employing a dual-channel mechanism to decouple the multi-source original sequences.Subsequently,bi-level complementarity regularization terms are introduced into the loss function to collaboratively optimize the model.At the system level,a global complementarity index is constructed based on normalized weighted aggregated sequences.This guides the model to mine multi-source complementary coupling features,thereby enhancing the overall prediction capability of the system.At the topological level,cosine similarity and a hinge function are combined to characterize the spatial correlation of electrically adjacent nodes.Furthermore,differentiated adjustments are implemented based on gating weights of real correlations to adaptively correct node prediction deviations.Finally,the STD-ST-Former model is trained and optimized using a loss function that integrates the bi-level complementarity regularization terms.This allows for the precise capturing of temporal evolution features while effectively characterizing the intrinsic complementarity mechanism,generating more accurate hydro-wind-photovoltaic joint prediction results.[Results]Experimental results based on multi-source power stations demonstrate that the proposed method exhibits higher prediction accuracy and more stable performance under complex spatiotemporal correlations.Compared to the seven baseline models,the relative Root Mean Square Error is reduced by 22.0%to 56.3%,the correlation coefficient is improved by 0.9%to 1.4%overall,and the prediction accuracy is consistently maintained above 96.9%.[Conclusions]The proposed approach based on bi-level complementarity regularization accurately reconstructs the true spatiotemporal evolutionary patterns of hydro,wind and PV power.It provides a feasible solution for multi-source collaborative forecasting in power systems with high penetration of renewable energy.