Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network

Abstract

With the fast growing of new energy technologies, the proportion of distributed renewable energy sources dominated by wind and light energy in the microgrid continues to increase. However, the uncertainty and randomness of energy itself bring challenges to the stable operation of the power system. Microgrid load forecasting with high accuracy is the key means to handle the above problems. It can provide help for power grid dispatching and decision-making, optimize resource allocation, reduce operation cost, and ensure system safety. In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, the criticality analysis of load influencing factors is carried out, and the clustering classification and weight calculation are completed. Then, the input data is preprocessed to ensure the quality of database. Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. Finally, based on the data of a local microgrid in Zhejiang Province, China, simulation experiments are conducted. The results are quantitatively compared with other forecasting algorithms to verify the accuracy and superiority of the proposed algorithm.