How renewable energy mix shapes day-ahead price predictability: A comparative analysis of solar, wind, and biomass in Czechia
再生可能エネルギーミックスが前日価格予測可能性に与える影響：チェコにおける太陽光、風力、バイオマスの比較分析 (AI 翻訳)
Štěpanec, Libor, zin lin, ohn, Hnin Yee Aye, Juchelkova, Dagmar
🤖 gxceed AI 要約
日本語
本研究は、チェコの電力市場において、太陽光、バイオマス、化石燃料といった異なる発電構成が前日価格予測精度に与える影響を機械学習モデルで分析した。太陽光比率が20%を超える時間帯では予測誤差が最大77%削減される一方、化石燃料優位時は誤差が大きい。SHAP解釈により、各変数の非線形的な影響を可視化している。
English
This study uses multiple machine learning models (Linear Regression, Random Forest, XGBoost, LightGBM) with SHAP interpretability to analyze how different renewable generation regimes (solar, biomass, fossil) affect day-ahead electricity price forecasting accuracy in the Czech market. Key finding: high solar penetration (>20% share) reduces forecasting error by up to 77% due to its predictable diurnal pattern, whereas fossil-dominated periods remain highly unpredictable.
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
日本では再生可能エネルギーの大量導入が進む中、系統安定性や市場設計への示唆が重要。本論文が示す太陽光発電の予測容易性と価格予測精度向上の関係は、日本の電力市場における再生可能エネルギー統合戦略に参考となる。
In the global GX context
This paper shifts the focus from renewable energy impact on price levels to predictability, a crucial dimension for grid stability and market efficiency. The use of ML and SHAP provides a replicable framework for analyzing structural changes in price formation under different renewable mixes, relevant to global electricity markets undergoing decarbonization.
👥 読者別の含意
🔬研究者:Provides a methodological framework combining ML forecasting with SHAP to analyze structural shifts in price formation due to renewable generation.
🏢実務担当者:Grid operators and energy traders can use findings on solar predictability to optimize bidding strategies and grid management.
🏛政策担当者:Insights on how renewable mix affects market predictability can inform regulatory design for renewable integration and capacity mechanisms.
📄 Abstract（原文）
While most existing literature focuses on how renewable energy sources (RES) influence day-ahead electricity price levels , this study shifts the focus toward market predictability and structural forecasting errors. We investigate how varying generation regimes (specifically Solar, Biomass, and Fossil Fuel dominance) fundamentally alter the accuracy of price forecasting models. By employing five diverse machine learning architectures—including Linear Regression, Random Forest, XGBoost, and LightGBM —and pairing them with SHAP (SHapley Additive exPlanations) interpretability tools, this research maps out the non-linear, structural shifts in price formation. Key Findings Included in the Study The Solar Effect: Hours with high solar penetration (>20% generation share) achieve up to a 77% reduction in forecasting error (Random Forest RMSE drops to 10.97 EUR/MWh). This is driven by the highly predictable, diurnal nature of solar output aligning with daytime peak demand. The Fossil Fuel Burden: Periods dominated by fossil-fuel thermal generation remain highly unpredictable, exhibiting the highest overall forecasting errors (RMSE of 48.53 EUR/MWh) due to sudden load swings, thermal network constraints, and severe price spikes. Baseload vs. Intermittent Dynamics: Biomass acts as a highly stable, dispatchable baseline with moderate stabilizing properties, while wind generation plays a negligible role due to current low capacity in the Czech bidding zone. Model Suitability: Due to strong temporal dependencies, an autoregressive Linear Regression serves as a robust baseline across the full market sample. However, tree-based models (Random Forest) are uniquely effective at mapping the intricate, non-linear interactions during peak renewable hours. Dataset Contents & Structure The repository provides hourly time-series tracking for the Czech Republic (CEPS bidding zone) , curated specifically to examine market behavior across a 20% renewable generation threshold. Variables include: Market Clearing Prices: Actual day-ahead electricity prices (EUR/MWh). Generation Profiles: Actual hourly power production profiles broken down by type (Solar, Biomass, and Fossil fuels). Solar Forecasts: Day-ahead solar generation forecasts vs. actual real-time generation. Grid Variables: Total actual grid electricity demand/load and net cross-border physical transmission flows (imports/exports). Metadata & Contact Information Dataset Creator: Ohn Zin Lin Institution: VSB - Technical University of Ostrava, Ostrava, Czechia Email: [email protected] ORCID: https://orcid.org/0000-0002-1927-6694 Data Collection Date: 2025-Dec-12 Article Acceptance Date: June 29, 2026 How to Cite If you utilize this dataset or the accompanying research findings in an academic or commercial capacity, please cite the primary publication: Ohn Zin Lin, (2026). How Different Renewable Energy Sources Shape Day-Ahead Electricity Price Predictability: Evidence from the Czech Market . Renewable Energy, Volume 26, 126146. https://doi.org/10.1016/j.renene.2026.126146
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21127559first seen 2026-07-03 04:14:56
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。