Multi Load Ultra Short-term Forecasting of Integrated Energy System Based on LSTNet-Skip

With the development of diversified energy demand on the user side, ultra short-term load forecasting is very important for the planning and optimization of dynamic large-scale integrated energy system. Therefore, this paper proposes a multivariate ultra short-term load forecasting model based on long-and short-term time-series network. First, convolutional neural network is used to extract the short-term dependence between multivariate loads. Then, the long-short term memory network is used to capture the long term dependence of load sequence, and the ultra long-term repetitive pattern of load sequence is fully studied by using the long-short term memory network with recurrent-skip. Finally, the autoregressive layer and full connection layer are used for combined prediction. mean absolute percentage error (MAPE) and root mean square error (RMSE) are used as evaluation indexes, the data set of the integrated energy system of Arizona State University Tempe campus is used for verification, and the three load forecasting methods are used for comparison. The experimental results show that the prediction models proposed in this paper are better than other methods and have higher prediction accuracy. ©2023 Chin.Soc.for Elec.Eng. 2273.