Point and interval forecasting of short-term electricity price with machine learning

Abstract

This research provides benchmark accuracies for forecasting of an aggregated price of the Dutch intraday market. While point forecasts in a single-step-ahead horizon for that unresearched market provide novel insights already, the scope of this research also includes interval forecasts in a multi-step-ahead horizon. A forecasting procedure is established that organizes several stages of in-sample and out-of-sample testing so that the number of arbitrary choices regarding features and hyperparameters is kept as low as possible. It is concluded on the basis of accuracies attained by naive, regression, and artificial neural network models that the machine learning models that are capable to incorporate linear and nonlinear relationships are able to infer to a varying degree what drives intraday from day-ahead prices. Furthermore, it is addressed whether superiority in terms of accuracy coincides with what is deemed as superior in practice. A simulation of a generic system, which consists of a battery and a wind turbine located in the Netherlands, smartly dispatches stored energy according to a schedule optimized with model predictive control based on point forecasts of intraday price. It is concluded that, in general, slightly higher profits are obtained with more accurate point forecasts and that different point forecasts lead to very different dispatch schedules that vary more than 10% in terms of dispatch frequency.