BENCHMARKING STATISTICAL, MACHINE LEARNING, DEEP LEARNING, AND HYBRID FORECASTING MODELS FOR GLOBAL RENEWABLE ENERGY CONSUMPTION: A WALK-FORWARD CROSS-VALIDATION STUDY WITH STRUCTURAL BREAK ANALYSIS
統計的、機械学習、深層学習、およびハイブリッド予測モデルを用いた世界の再生可能エネルギー消費のベンチマーク：構造変化分析を伴うウォークフォワード交差検証研究 (AI 翻訳)
Shaon Biswas, Paramita Roy
🤖 gxceed AI 要約
日本語
本論文は、世界の再生可能エネルギー消費量（World Bank EG.FEC.RNEW.ZS指標、1990-2020年）を対象に、13種類の予測モデルを統一的なウォークフォワード交差検証プロトコルでベンチマークした。ハイブリッドETS-GRUモデルを含む諸モデルを3年先予測で比較し、統計的優位性を評価した。再生可能エネルギー普及の予測精度向上に資する。
English
This paper benchmarks 13 forecasting model families on global renewable energy consumption (World Bank indicator, 1990-2020) using walk-forward cross-validation with a 3-year horizon. It evaluates models via Diebold-Mariano tests and Model Confidence Set analysis, providing insights for energy transition planning and SDG-7 monitoring.
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GX文脈では、再生可能エネルギー導入目標の達成度を定量評価するための予測手法の精度向上は重要である。本論文のベンチマーク手法は、エネルギー基本計画やGXロードマップにおける再エネ比率目標の達成可能性検証に活用可能。SSBJ開示のシナリオ分析にも応用できる。
In the global GX context
In the global GX context, accurate forecasting of renewable energy consumption is critical for monitoring SDG-7 and net-zero targets. This rigorous benchmarking provides a methodological foundation for scenario analysis and capacity planning, relevant for ISSB and transition finance assessments.
👥 読者別の含意
🔬研究者:This study offers a comprehensive model comparison for energy forecasting researchers, with robust cross-validation and statistical tests.
🏢実務担当者:Corporate sustainability teams can adopt the forecasting framework for internal energy transition planning and reporting.
🏛政策担当者:Policy offices can use the model evaluation results to improve renewable energy projections for target setting and policy assessment.
📄 Abstract（原文）
Energy independence and resilience have become critical policy priorities as geopolitical tensions, supply disruptions, and price volatility expose the vulnerability of fossil-fuel-dependent energy systems. Accurate forecasting of renewable energy consumption is therefore essential for effective energy transition planning, infrastructure investment, and monitoring progress toward international climate targets such as Sustainable Development Goal 7 (SDG-7). In macro-energy policy practice, quantitative forecasts underpin scenario design, capacity planning, and assessment of alignment with net-zero pathways, yet the annual frequency and short length of globally comparable time series severely constrain the effective application of data-intensive forecasting methods. This study benchmarks 13 forecasting model families-spanning baselines (Naive, Random Walk with Drift, Linear Trend), classical statistical methods (ETS, Damped ETS, Theta, ARIMA), machine learning (XGBoost), deep learning (GRU, LSTM, N-BEATS), an additive model (Prophet), and a novel ETS-GRU hybrid - against the World Bank EG.FEC.RNEW.ZS indicator (1990-2020). All models are evaluated under a unified 5-window expanding walk-forward cross-validation protocol with a 3-year forecast horizon, nested hyperparameter tuning, multi-seed deep learning robustness checks, Diebold–Mariano tests, Model Confidence Set analysis, and bootstrap inference.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.21474/ijar01/23124first seen 2026-05-15 17:56:29
🔔 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。