An optimised LSTM algorithm for short-term load forecasting

Abstract

Load forecasting is a basic work of power dispatching, planning and other departments, and has always attracted attention from inside and outside the industry. The purpose of this is to make the extracted features nonlinear and to be able to learn more complex knowledge. The improved LSTM network is introduced to make a short-term forecast of wind power load. The dataset used in this experiment is the foreign GEFcom2014-Load wind power dataset. Because the dimension units of the 24-dimensional impact indicators in the dataset are different, in order to eliminate the dimensional impact between the indicators, the data is normalised in the experiment by adopting the min-max standardisation method. The experimental results show that the training error, validation error, and test error of the improved method are all reduced by 1%, 18% and 16%, compared with the second, third, and fourth groups.