Long and short-term power supply and demand forecasting based on time series analysis under high proportion clean energy integration

Abstract

This paper focuses on the prediction of power supply and demand in the electric power system under a high proportion of clean energy integration using time series analysis. Firstly, the impact of clean energy integration on power supply and demand is analyzed, taking into account factors such as the volatility and seasonality of renewable energy sources. A case study is conducted in Region A, which comprises four areas in China, to forecast the power supply and demand in the electric power system. In terms of power demand, we employ two commonly used methods in time series analysis, namely SARIMAX and factor decomposition, to establish a comprehensive forecasting model. These methods are applied from both short-term and long-term perspectives to analyze the monthly maximum electricity demand of users, aiming to accurately predict power demand under a high proportion of clean energy integration. As for power supply, we utilize the least squares method to regressively fit the installed capacity of energy sources and analyze future trends. Through the predictions of both demand and supply, the stable operation of the power system is ensured.