欧州12入札ゾーンにおける翌日負の電力価格予測：ゲート閉鎖監査付き説明可能機械学習フレームワーク
Predicting Negative Day-Ahead Electricity Prices Across 12 European Bidding Zones: A Gate-Closure-Audited Explainable Machine Learning Framework (原題)
T. Rokicki, P. Bórawski, Aneta Bełdycka-Bórawska, Bogdan Klepacki
🤖 gxceed AI 要約
日本語
欧州12入札ゾーンの2019〜2025年の73万件超のデータを用い、翌日負の電力価格を予測するXGBoostフレームワークを構築。入札前変数（価格履歴・カレンダー・構造的負荷）が強い予測信号となり、市場間の移転性は部分的で地域校正が必要と判明。負の価格は柔軟性不足の直接証拠ではなく、余剰吸収が限られる市場構成のスクリーニング信号と解釈される。
English
Using 736,000+ observations from 12 European bidding zones (2019–2025), this study builds a gate-closure-audited XGBoost framework to predict negative day-ahead electricity prices. Pre-auction signals (price history, calendar, structural load) dominate, while leave-one-market-out validation shows only partial transferability, supporting local calibration. Negative prices are framed as screening signals for limited surplus absorption, not direct evidence of flexibility shortfall.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも再エネ拡大に伴い負の電力価格や出力制御が増加しており、本論文の入札前予測・市場診断の枠組みは、需給調整市場や柔軟性リソースの設計、発電事業者のリスク管理に示唆を与える。
In the global GX context
As variable renewables grow, negative pricing is spreading globally; this framework offers a reproducible early-warning tool for market participants and regulators, complementing TCFD/ISSB transition-risk analysis by quantifying price-regime shifts in power markets.
👥 読者別の含意
🔬研究者:機械学習による電力価格予測の手法と、市場間移転性の限界に関する実証的知見を提供する。
🏢実務担当者:再エネ発電事業者や電力トレーダーが負の価格リスクを事前に把握し、ヘッジや運用戦略に活用できる。
🏛政策担当者:柔軟性リソースの不足を直接示すものではないが、市場設計や容量メカニズムの検討に資するスクリーニング指標を提供する。
📄 Abstract（原文）
The growing penetration of variable renewable energy (VRE) is increasing the frequency of very low and negative prices, although these events also depend on demand, transmission capacity, price-regime persistence and flexibility resources. This study examines which pre-auction and diagnostic variables are associated with negative day-ahead prices across European bidding zones, and whether these relationships remain stable over time and transferable across markets. More than 736,000 observations from 12 bidding zones in 2019–2025 were analysed, with sample coverage varying by data completeness. A gate-closure-audited Extreme Gradient Boosting (XGBoost) model achieved moderate risk-ranking performance and positive probabilistic skill in 2024–2025. The strongest pre-auction signals were completed-auction price history, calendar features and structural load relationships. Leave-one-market-out validation showed partial and heterogeneous transferability, supporting local calibration. A separate diagnostic layer revealed market-specific, non-linear associations involving VRE forecasts, residual load and cross-border exchange. Negative prices are therefore interpreted as screening signals for market configurations potentially associated with limited surplus absorption, rather than direct evidence of a flexibility shortfall. The framework separates operational pre-auction prediction from later market diagnosis and provides a reproducible basis for local early-warning applications. All SHAP, ALE and scenario results are interpreted as predictive associations and model diagnostics, not as causal effects or direct measures of physical flexibility.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/app16189063first seen 2026-09-19 05:56:03 · last seen 2026-09-22 05:22:50
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