Abstract: There are many studies on the load forecasting of large power grids and relatively few studies on microgrids. Therefore, it is very important to establish a suitable microgrid load forecasting model to improve the accuracy of forecasting. This paper analyzes and selects temperature, daily type, and multiple historical loads as the input variables of the model for the case of fewer input variables, selects the LSTM neural network based on the recurrent neural network for modeling, and constructs the load forecasting model of microgrid based on LSTM neural network. Finally, in order to enhance the reliability of the results, two sets of load data in different time periods are used to predict separately, and the prediction results of the LSTM neural network are compared with those of BP neural network, RBF neural network and Elman neural network. The experimental results show that the prediction results of LSTM neural network are better than BP neural network, RBF neural network and Elman neural network. The LSTM neural network load forecasting model has good promotion prospect under the background of microgrid.

Key words: microgrid, short-term load forecasting, recurrent neural network, LSTM neural network