Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer

Abstract

Accurate forecasting of power plant loads is critical for maintaining a stable power supply, minimizing grid fluctuations, and enhancing power market trading mechanisms. However, the data on power plant generation load (hereinafter abbreviated as load) are non-stationary. The focus of existing load forecasting methods has been on continuously improving the ability to capture the dependent coupling between outputs and inputs, while research on external factors, which are the causes of non-stationary load data, has been neglected. The identification of the causal relationship between external variables and load is a significant factor in accurately predicting load. In the present study, the causal effects of various external variables on load were identified and then quantitatively calculated using various methods. Based on the improved Informer model, a long-time series forecasting model, a hybrid forecasting method was proposed called causal inference-improved Informer (hereinafter abbreviated as Causal–Informer). In the present study, the mutual information method was used to remove insignificant external variables. Subsequently, external factors such as GDP, holidays, ambient temperature, wind speed, power plant maintenance status, and rainfall were selected as input features of the proposed forecasting model. Finally, the proposed Causal–Informer method was evaluated using the historical load of a power plant in East China. Compared with four popular forecasting models, measurements on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for the proposed method were reduced by 89.8 million kwh–672.3 million kwh, 56.8 million kwh–637.9 million kwh, and 5.1–25.4%. The proposed method achieved the most accurate and stable results. The MAPE reached 10.4% and 24.8% in 30 time steps ahead and 90 time steps ahead of forecasts, respectively.