Hybrid Stacking with Targeted Residual Learning for PV Forecasting
PV予測のためのターゲット残差学習を用いたハイブリッドスタッキング (AI 翻訳)
Khawla Oufrit, Abdelkader Mouadili, Mimoun Zazoui
🤖 gxceed AI 要約
日本語
太陽光発電の予測精度向上のため、スタッキングアンサンブルと残差学習を組み合わせたハイブリッドフレームワークを提案。Random Forest、Extra Trees、XGBoostを統合し、Ridge回帰でメタ学習。誤差の大きいサンプルを特定し、専用の補正モデルを適用。実データで評価し、Extra Treesが最良の性能を示し、気象条件による誤差変動を分析。
English
This study proposes a hybrid PV forecasting framework combining stacking ensemble learning with targeted residual correction. It integrates Random Forest, Extra Trees, and XGBoost with a Ridge meta-learner, and applies a correction model to hard-to-predict samples. Evaluated on real desert-climate PV data, Extra Trees achieved best performance, and error analysis revealed strong influence of irradiance variability.
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
日本の再生可能エネルギー導入拡大に伴い、PV予測の精度向上は電力系統の安定運用に直結する。FIT制度から市場統合への移行期において、予測誤差の低減はインバランス料金の削減や需給調整コストの低減に寄与し、事業者の収益性向上につながる。
In the global GX context
Globally, as solar penetration increases, accurate PV forecasting is critical for grid stability and market integration. This work addresses error heterogeneity, a key challenge in variable weather conditions, offering a robust framework that can be adapted to different climates and grid contexts, supporting the transition to renewable-based power systems.
👥 読者別の含意
🔬研究者:Provides a novel hybrid framework combining stacking and residual learning, with insights into error distribution analysis for PV forecasting.
🏢実務担当者:Offers a practical forecasting method that can reduce imbalance costs and improve operational efficiency for solar plant operators and grid managers.
🏛政策担当者:Highlights the importance of forecasting accuracy for renewable integration, informing policies on grid modernization and renewable support mechanisms.
📄 Abstract（原文）
Photovoltaic (PV) power forecasting is essential for the reliable integration of solar energy into modern power systems. Although recent machine learning and ensemble-learning models achieve high forecasting accuracy, their performance often deteriorates under variable weather conditions, leading to large prediction errors and reduced reliability. This limitation highlights the need for forecasting frameworks that explicitly address error heterogeneity rather than focusing solely on global performance metrics.This study proposes a hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy. The stacking architecture integrates three complementary ensemble models, namely Random Forest, Extra Trees, and XGBoost, whose outputs are combined through a Ridge regression meta-learner. To improve forecasting robustness, a residual-learning mechanism is introduced to identify difficult observations based on prediction errors and irradiance conditions. A dedicated correction model is subsequently applied to these hard-to-predict samples.The proposed framework is evaluated using real-world PV generation and meteorological data collected from a desert-climate photovoltaic installation. Experimental results show that ensemble-based models achieve excellent overall forecasting accuracy, with Extra Trees providing the best global performance. Furthermore, weather-regime and sensitivity analyses reveal that forecasting errors are strongly influenced by irradiance variability and atmospheric conditions. The findings demonstrate that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photovoltaic forecasting systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://www.epj-conferences.org/10.1051/epjconf/202638100024/pdffirst seen 2026-08-12 04:47:33
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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。