WITHDRAWN: Short-term Load Forecasting Method Based on LSTM Optimized by the Hybrid Dung Bettle Optimization Algorithm

Abstract

Forecasting electricity load is a crucial responsibility for the power system. Precise short-term power load forecasting (STLF) is a valuable tool for regional production planning, energy conservation, emission reduction, and grid coordination and dispatch. This study suggests a model for STLF that makes use of an optimized Long and Short-Term Memory (LSTM) neural network through the Hybrid Dung Beetle Optimization algorithm (HDBO-LSTM). Firstly, the Hybrid Dung Beetle Optimization (HDBO) algorithm is used to optimize the LSTM network's hyperparameters to address the problem that the LSTM network's random hyperparameter selection will materially influence the accuracy of load forecasting. Secondly, to address the problems that the original Dung Beetle Optimization (DBO) algorithm has poor global exploration ability and is easy to falls into local optimization, multiple improvement strategies are proposed to enhance the DBO algorithm, and the HDBO algorithm, which has a stronger searching ability and faster convergence speed, is proposed. By comparing with several algorithms on 10 benchmark functions, HDBO shows better search performance. Finally, the HDBO-LSTM load forecasting model is developed and evaluated using the Electrotechnical Mathematical Modeling Competition's power load dataset. The outcomes show that HDBO-LSTM outperforms the other comparison models in terms of accuracy and predicting efficacy.