Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. (1st July 2021) - Lago, Jesus; Marcjasz, Grzegorz; De Schutter, Bart; Weron, Rafał
Abstract: While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies. Highlights: A set of guidelines for electricity price forecasting (EPF) research are suggested. A review of state-of-the-art algorithms in EPF is presented. Two open-source forecasting models for EPF are made publicly available. An open-access benchmark dataset for evaluation of future EPF studies is provided. An open-source toolbox to advance the field of EPF is proposed.