Short-term Load Forecasting Considering Meteorological Factors and Electric Vehicles

Abstract

A short-term load forecasting method considering meteorological factors and electric vehicles is essential to the successful operation of the power system. This paper proposes a unique short-term load forecasting method based on neural network. First, through the analysis of typical daily load data, it is demonstrated that the short-term load data changes with the daily, weekly, weather type and the charging of electric vehicles. Then, the load forecasting model based on the neural network is set up with historical data, meteorological data and electric vehicle charging data as input. Finally, the prediction model is simulated to improve the accuracy of load forecasting.