Hybrid Seq2Seq-ARIMA Load Forecasting for Power Systems with Metaheuristic Hyperparameter Optimization

Abstract

In power grid dispatching and planning, the accuracy of electricity demand plays a vital role in the safety and economy of the power grid. In view of the problems existing in the current load forecasting of the power grid, a long-term and short-term hybrid model is studied to improve the accuracy and robustness of load forecasting. This project intends to combine the advantages of Seq2Seq model in time series analysis with ARIMA's advantages in stability to effectively solve the supply and demand relationship in long and short cycles. First, considering the nonlinear characteristics of power demand in the power market, a hybrid modeling framework based on optimality is constructed. It is optimized using methods such as genetics and particle swarms. Secondly, the constructed model is empirically analyzed using simulation experiments, and it is found that the constructed method has excellent accuracy on multiple time scales. Especially in the volatile power market environment, it has better robustness and adaptability. After precise data verification, the average error rate of short-term prediction of this model is within 5%, and within 7% in the longer period.