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Forecasting U.S. Renewable Energy Consumption Using Advanced Machine Learning, Deep Learning, and Time-Series Foundation Models: A Monthly Multisector Benchmarking and Planning Analysis

先進的な機械学習、深層学習、時系列基盤モデルを用いた米国の再生可能エネルギー消費の予測：月次マルチセクターベンチマーキングと計画分析 (AI 翻訳)

Leyla Zhuhadar

Sustainability📚 査読済 / ジャーナル2026-07-02#再生可能エネルギーOrigin: US経営インパクト: コスト削減対象セクター: cross_sector
DOI: 10.3390/su18136730
原典: https://doi.org/10.3390/su18136730

🤖 gxceed AI 要約

日本語

本研究は、米国の再生可能エネルギー消費を予測するための統合的枠組みを開発。1973年から2025年までの月次データを用い、商業、電力、産業、住宅、運輸の5セクターを対象に、機械学習、深層学習、時系列基盤モデルを比較。結果、特徴ベースのツリーモデルが多くの深層学習モデルに勝る性能を示し、階層的計画評価がモデル選択に重要であることを明らかにした。

English

This study develops an integrated framework for forecasting U.S. renewable energy consumption using monthly multisector data from 1973-2025, comparing machine learning, deep learning, and time-series foundation models. Results show that feature-based tree models outperform many deep learning architectures, and hierarchical planning evaluation is crucial for model selection.

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普及計画においても、本フレームワークの手法（階層的調整、計画評価）は有用である。特に、セクター別・電源別の予測と計画策定に応用可能。

In the global GX context

This paper provides a comprehensive benchmarking framework for renewable energy forecasting that is globally applicable, especially for countries scaling up renewables. The comparison of foundation models with traditional methods offers practical guidance for energy planners.

👥 読者別の含意

🔬研究者:This paper provides a rigorous benchmarking framework that can be replicated for other countries and includes insights on model selection for energy forecasting.

🏢実務担当者:Energy planners and utilities can use the planning-focused evaluation to select models that support decision-making for renewable energy integration.

🏛政策担当者:Policymakers can leverage the scenario analysis to understand trade-offs between solar acceleration and diversification in renewable portfolios.

📄 Abstract（原文）

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims.

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