| Citation:Kailang Wu,Jie Gu,Lu Meng,Honglin Wen,Jinghuan Ma.An explainable framework for load forecasting of a regional integrated energy system based on coupled features and multi-task learning[J].Protection and Control of Modern Power Systems,2022,V7(2):349-362[Copy] |
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| Abstract: |
| To extract strong correlations between different energy loads and improve the interpretability and accuracy for load
forecasting of a regional integrated energy system (RIES), an explainable framework for load forecasting of an RIES is
proposed. This includes the load forecasting model of RIES and its interpretation. A coupled feature extracting strat
egy is adopted to construct coupled features between loads as the input variables of the model. It is designed based
on multi-task learning (MTL) with a long short-term memory (LSTM) model as the sharing layer. Based on SHapley
Additive exPlanations (SHAP), this explainable framework combines global and local interpretations to improve the
interpretability of load forecasting of the RIES. In addition, an input variable selection strategy based on the global
SHAP value is proposed to select input feature variables of the model. A case study is given to verify the effectiveness
of the proposed model, constructed coupled features, and input variable selection strategy. The results show that the
explainable framework intuitively improves the interpretability of the prediction model. |
| Key words: Load forecasting, Regional integrated energy system, Coupled feature, SHapley additive exPlanations,
Interpretability of deep learning |
| DOI：10.1186/s41601-022-00245-y |
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| Fund:This work was supported in part by the National Key Research Program of
China (2016YFB0900100) and Key Project of Shanghai Science and Technology
Committee (18DZ1100303). |
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