Optimal Selection of Weather Stations for Electric Load Forecasting

Abstract

For the proper planning and operation of any electric power system, it is essential to have a reliable software tool that allows for accurate short-term forecasting of electric power consumption. Temperature is one of the most important drivers of electric energy consumption and selecting the appropriate weather station or combination of weather stations is crucial for improving the forecasting accuracy. In this study, we propose an algorithm to determine the optimal selection of weather stations based on (i) an initial reduction in problem complexity, which drastically decreases the computational cost, and (ii) a posterior incremental search algorithm. The proposed methodology, which identifies the optimal number of weather locations to be used and their specific geographical locations, is exhaustively tested using three insightful case studies using real data from the Spanish mainland electric power grid and two different weather forecast databases, denoting a significant reduction in forecasting errors. The developed method is currently in use by the Spanish transmission system operator (Red Eléctrica de Espa na, REE) to make hourly forecasts.

Keywords

• Short-term electric load forecasting
• time series
• meteorological variables
• seasonal Reg-ARIMA models