Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

TL;DR

This paper reviews advanced electricity price forecasting methods, highlights evaluation challenges, and provides datasets, benchmarks, and best practices to improve future research in the field.

Contribution

It offers a comprehensive survey, introduces a standardized benchmark, and releases datasets and tools for rigorous evaluation of forecasting algorithms.

Findings

State-of-the-art models are compared across multiple markets and years.

Benchmarking reveals the performance of various statistical and deep learning methods.

The paper proposes best practices for evaluating electricity price forecasting models.

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 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.