国境を越えた電力価格予測のための深層学習：比較研究

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:電力価格予測における深層学習モデルの比較評価と再現可能なベンチマーク設計に関心のある研究者に有用。

🏢実務担当者:電力トレーディングや調達リスク管理に携わる実務者にとって、限られたデータでの予測モデル選定の参考になる。

🏛政策担当者:再エネ大量導入下での電力市場安定化や価格予測精度向上に関心のある政策担当者に示唆を与える。

While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many studies have relied on different datasets and metrics to evaluate methods in isolated settings, making it difficult to assess progress and compare state-of-the-art approaches consistently. In this work, we use public data to evaluate deep learning models for electricity price forecasting (EPF) across multiple market settings. Our goal is to establish a reproducible framework that enables a consistent evaluation of forecasting models. Developing standardized benchmarks for EPF is particularly important given the growing complexity of electricity markets, driven by the increasing integration of renewable energy sources. Their volatility increases the supply uncertainty and creates additional forecasting challenges. Under these conditions, accurate EPF methods support operational efficiency, energy trading, and grid stability. Although deep learning has been explored for day-ahead EPF, many prior studies are limited to single-market settings, narrow feature sets, or fixed training regimes. This work presents a comparative evaluation of six deep learning models–covering state-space, MLP, RNN, and Transformer-based architectures–emphasizing generalization across markets. We simulate low-data target-market conditions using zero-shot, one-shot, and few-shot learning. Our test set focuses on the Germany–Luxembourg (DE-LU) bidding zone in 2024 using a standardized dataset with calendar, historical price, and market-derived features. Our findings suggest that N-HiTS and NBEATSx perform competitively in limited-data scenarios, while transformer-based models can reach comparable accuracy but tend to require more adaptation and tuning. Model performance also benefits from careful feature selection and hyperparameter tuning, and we note that the differences between the strongest models are often small.

• semanticscholar https://doi.org/10.1109/access.2026.3727000first seen 2026-09-15 05:17:11 · last seen 2026-09-22 05:23:00