Opposition learning & PID-based grey wolf optimizer with swarm intelligence for improved load forecasting

Electricity load forecasting helps grid operators to make informed decisions in terms of planning and managing demand response. Electric power companies utilize load forecasting to make optimal power management. Therefore, accurate forecasting of total electrical load in a region is of great importance. To overcome this problem, this paper proposes a multi-layer perceptron (MLP) hybrid model that contain Swarm Decomposition (SWD) aided Opposition Learning and proportional–integral–derivative based Grey Wolf Optimizer (OLPIDGWO) using historical electricity demand data in non-consecutive years. The dataset used for load forecasting includes loads with different characteristics. Empirical mode decomposition method and swarm decomposition are applied to the original data to decompose the data features. Then, MLP hybrid model is applied for each decomposed signal of the data as the load forecasting model. The advantages of the proposed hybrid model include a significant improvement in forecast accuracy and capture of local maxima. The advantage of the proposed hybrid model over other hybrid models and existing single forecasting models is also verified by error performance metrics. The result of the hybrid forecast model shows that the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2020 are 35 MW, 0.591MW, 0.452MW and 1.47%, respectively, and the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2022 are 22.6MW, 0.475MW, 0.367MW and 1.21%, respectively. The results reveal the SWD decomposition and GWO optimizer module of MLP improve the load prediction, and the proposed model outperforms other load prediction models.