Load forecasting model considering dynamic coupling relationships using structured dynamic-inner latent variables and broad learning system

Integrated energy systems (IES) can effectively regulate and optimize dynamic loads by utilizing load forecasting, which intelligently manages energy scheduling. Nevertheless, the insufficient attention given by existing research to loads with multiple frequency scales and dynamic coupling relationships in the IES may lead to a reduced accuracy in load forecasting. To address this issue, a novel load forecasting model using structured dynamic-inner latent variables and broad learning system (SDiLV-BLS) is proposed. Firstly, the dynamic coupling reconstruction method is introduced to restructure the data of various load types into multimodal data with coupling relationships across multiple frequency scales, serving as input for the forecasting model. Then, the structured dynamic-inner latent variables (SDiLV) decouple the multimodal data to obtain dynamic features that have regression properties and simplify the complexity of the input features for the forecasting model. Finally, the broad learning system (BLS) is employed to reveal the structured geometric patterns in the extracted features, which achieves accurate load forecasting. Case studies show that by employing the coupling feature selection strategy in SDiLV-BLS, there is an improvement in forecasting accuracy compared to the strategy of selecting only load features of the same type and the strategy of selecting all load type features. Furthermore, by selecting coupling features, the average root mean square error (RMSE) and mean absolute error (MAE) of SDiLV-BLS are reduced by 21.66% and 22.06%, respectively, compared to the original BLS in multi-step ahead forecasting.