The Role of Weather Predictions in Electricity Price Forecasting Beyond the Day-Ahead Horizon

Abstract

Forecasts of meteorology-driven factors, such as intermittent renewable generation, are commonly included in electricity price forecasting models. We show that meteorological forecasts can be used directly to improve price forecasts multiple days in advance. We introduce an autoregressive multivariate linear model with exogenous variables and LASSO for variable selection and regularization. We used variants of this model to forecast German wholesale prices up to ten days in advance and evaluate the benefit of adding meteorological forecasts, namely wind speed and direction, solar irradiation, cloud cover, and temperature forecasts of selected locations across Europe. The resulting regression coefficients are analyzed with regard to their spatial as well as temporal distribution and are put in context with underlying power market fundamentals. Wind speed in northern Germany emerges as a particularly strong explanatory variable. The benefit of adding meteorological forecasts strongest when autoregressive effects are weak, yet the accuracy of the meteorological forecasts is sufficient for the model to identify patterns. Forecasts produced 2-4 days in advance exhibit an improvement in RMSE by 10-20%. Furthermore, the forecasting horizon is shown to impact the choice of the regularization penalty that tends to increase at longer forecasting horizons.