A review of imbalance price forecasting algorithms in Europe: algorithms, metrics and the way forward

TL;DR

This paper reviews imbalance price forecasting algorithms in European power markets, emphasizing data quality, benchmarking needs, and evaluation criteria for accuracy and value.

Contribution

It provides a comprehensive comparison of methodologies, highlights the importance of input data quality, and advocates for standardized benchmarks and evaluation metrics.

Findings

Data-driven machine learning models are now predominant.

High-quality input data, including intraday and minute data, is crucial.

A need for common benchmarks and evaluation standards is identified.

Abstract

Renewable electricity generation has grown significantly across many European power systems, leading to a greener energy mix, but also additional complexity in balancing electricity supply and demand. Unexpected differences between forecasts and actual output can lead to fluctuations in the system imbalance, which causes volatile imbalance prices. Accurate imbalance price forecasts are crucial for market players to choose a strategic balancing position. In early works, most forecasting methods combined fundamental and statistical approaches, but currently there is a clear trend towards data-driven machine learning models. This review compares forecasting algorithms in European markets with a focus on methodology. We emphasize the importance of high-quality input data, including intraday information and per-minute system data. Next, we identify the need for a common benchmark to compare novel forecasting methods developed for different markets and time periods. Finally, we argue that forecasts should be evaluated in terms of both downstream value and accuracy.