DBO-LSTM-based Demand-side Resource Load Forecasting Method

A combined forecasting method based on Dung Beetle Optimization (DBO) and Long Short-Term Memory (LSTM) network is proposed to address the problems of strong nonlinearity of demand-side user load data and the difficulty of forecasting. Firstly, under the consideration of the weather influence factor, the DBO optimizes the parameters of LSTM network, and explores the feature correlation and time sequence correlation between the data, and finally obtains the load prediction result of a typical demand response day. The experiment adopts the actual electricity load data of a region in China, and after comparing and analyzing with the typical LSTM prediction model, it is verified that the proposed model has higher prediction accuracy and greatly reduces the relative error of load prediction.