Deep learning-based electricity price forecasting: Findings on price predictability and European electricity markets
Year: 2024
Type: article
Abstract: Deep learning algorithms have transformed day-ahead electricity price predictions in recent years. This phenomenon has implications for price predictability and perceived risks, which in turn could impact investments. This study aims to unravel the impact of deep learning-based price forecasting (DPF) on price predictability and its correlation with price volatility. This has been done by examining 19 bidding zones across 11 European countries from January 2018 to June 2023. This timeframe captures the unique market dynamics influenced by the COVID-19 pandemic and the Global Energy Crisis. This analysis has been enabled by the development of the Day-ahead European Electricity Price Forecasting Kit (Deepforkit), a forecasting tool developed in-house and open-sourced. Notably, Deepforkit offers a high running speed, enabling large-scale and multi-market analyses such as the one conducted in this study. It is quantitatively proven that if DPF is used, price volatility is just moderately correlated to unpredictability, and using it as a proxy for price unpredictability is not reliable. This finding has an important implication on the investment risk perception and the cost of capital for new investments in the power sector. Moreover, in this study, the predictability behavior of prices is compared in 3 markets and 11 European countries.
Source: Energy
Authors: Kasra Aliyon, Jouni Ritvanen
Institution: Lappeenranta-Lahti University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,140
OpenAlex ID: W4401741919