A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

Yu Jin,Jianzhou Wang,Honggang Guo,Aiyi Song

doi:10.3390/en13236241

Abstract

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

Highlights

Along with fast-economic development, power enterprises continue to expand their construction scales, and the corresponding power grid structures and operation modes are gradually becoming diversified [1]
(c) For South Australia, from the perspective of the annual average prediction accuracy, in all prediction models, the performance evaluation index values calculated by the VLG hybrid model were significantly better than the performance index values calculated by the other prediction model processing methods, and its mean absolute percentage error (MAPE) value was 0.9800%
Values are 0.2602%, 0.3718%, 0.3101%, and 0.3758%, respectively. It can be seen from the MAPE values that using the binary encoding genetic optimization algorithm (BEGA) algorithm to select the optimal unit length L and the number of cell units N can increase the model accuracy by up to 30%

Summary

Introduction

Along with fast-economic development, power enterprises continue to expand their construction scales, and the corresponding power grid structures and operation modes are gradually becoming diversified [1]. Because electricity is a special type of energy, people cannot store much electricity. This requires the power generation capacity of power generation enterprises and the power supply capacity of power supply companies to maintain a state of dynamic balance; otherwise, the lives of residents and the production of enterprises will be affected, potentially endangering the security and availability of the whole electrified wire netting system. Accurate power load prediction provides an important guarantee to ensure that the power supply and demand remain in a stable state. An accurate power load will yield significant economic benefits. Improving the prediction performance of electrical load prediction will provide a solid foundation for the smooth operation of the power grid and provide theoretical support for power supply and dispatching plans

Methods

Results

Discussion

Conclusion