Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics

—Accurate multi-energy load forecasting (MELF) is the key to realize the balance between supply and demand in regional integrated energy systems (RIES). To this end, a hybrid MELF method for RIES considering temporal dynamic and coupling characteristics (MELF_TDCC) is proposed. The novelty of MELF_TDCC lies in the following three aspects: 1) considering the high-dimensional temporal dynamic characteristic, an encoder-decoder model based on long-short term memory network (LSTMED) is proposed, which can extract the high dimensional potential feature, and reflect the temporal dynamic characteristics of historical load sequence effectively; 2) considering the cross-coupling characteristic, a coupling feature matrix of multi-energy load is constructed, which reflects the cross-influence of electricity, cooling and heating loads; 3) with the feature fusion layer of the hybrid model being built by gradient boosting decision tree (GBDT), the extended feature matrix for each class of load is constructed considering the intra-class inherent characteristics and inter-class coupling characteristic of loads, and the GBDT model is trained on the extended feature matrix, which provides multi-dimensional perspective for researching load essential characteristics. MELF_TDCC is verified on the ultra-short-term and short-term MELF scenarios based on an actual dataset. The simulation result shows that the proposed MELF_TDCC outperforms the current advanced methods.

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