Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
This work provides a standardized benchmark and guidelines to improve the reliability and comparability of forecasting methods in the electricity market, though it is incremental as it consolidates existing approaches rather than introducing new algorithms.
The paper addresses the lack of rigorous evaluation in electricity price forecasting by conducting a literature survey, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and proposing best practices, while providing open-access datasets and tools for future research.
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 performing a literature survey of state-of-the-art models, 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.