再生可能エネルギーコミュニティ最適化のためのデジタルツインに着想を得たシミュレーションフレームワーク
A Digital Twin Inspired Simulation Framework for Optimizing Renewable Energy Communities (原題)
João Oliveira, Tiago Santos, Fernanda Brito Correia, José Torres Farinha, Róisín Monteiro, Mateus Mendes
🤖 gxceed AI 要約
日本語
本研究は、再生可能エネルギーコミュニティ（REC）の計画を支援するため、デジタルツインに着想を得たシミュレーションフレームワークを提案する。高解像度スマートメータデータと人口統計フィルタリング、AIベースのN-HiTS予測モデルを統合し、ポルトガルのクルアトラ島エネルギーコミュニティに適用した。PV拡大とBESS導入のシナリオを評価し、最適構成では自給率37.3%、自家消費率99.8%を達成した。
English
This study proposes a Digital Twin-inspired simulation framework to support renewable energy community (REC) planning. It integrates high-resolution smart-meter data, demographic filtering, and an AI-based N-HiTS forecasting model, applied to the Culatra Island Energy Community in Portugal. Scenarios with PV expansion and BESS deployment were evaluated, achieving a self-sufficiency rate of 37.3% and self-consumption of 99.8% in the optimal configuration.
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地産地消やコミュニティエネルギーが注目されており、本フレームワークは地域のエネルギー計画やFIT終了後の自立分散型システム構築に示唆を与える。また、AI予測と実データを組み合わせた手法は、日本のスマートメータデータ活用や再エネ導入計画に応用可能。
In the global GX context
Globally, this work contributes to the growing literature on renewable energy communities and digital twins, offering a replicable methodology for using smart-meter data and AI forecasting to optimize storage and PV sizing. It provides empirical evidence from a real island community, supporting the energy transition and decentralized resource management.
👥 読者別の含意
🔬研究者:Provides a novel integration of AI forecasting with simulation for REC planning, offering a methodological template for future studies.
🏢実務担当者:Offers a data-driven approach to optimize PV and battery sizing in community energy projects, improving self-sufficiency and reducing grid dependence.
🏛政策担当者:Demonstrates the potential of AI and smart-meter data to support community energy planning, informing policies for renewable energy adoption.
📄 Abstract（原文）
The energy transition requires efficient management of decentralized resources, in which Renewable Energy Communities (RECs) play an increasingly important role. However, the variability of solar generation and the unpredictability of consumption create complex balancing challenges. To address the limitations of existing planning tools—which often rely on synthetic profiles or small-scale validations—this study presents a data-driven Digital Twin-inspired simulation framework The unique contribution of this work lies in the combination of three elements: the use of high-resolution sub-hourly smart-meter data, the application of a novel demographic filtering methodology to accurately isolate permanent community load profiles, and the integration of an AI-driven N-HiTS (Neural Hierarchical Interpolation for Time Series) forecasting model. The framework was implemented using the PyECOM simulation engine and applied to the Culatra Island Energy Community, Portugal, processing empirical data from 338 dwellings. Multiple scenarios were evaluated, including demand flexibility, photovoltaic (PV) expansion, and battery energy storage (BESS) deployment. The baseline scenario revealed a substantial dependence on the external grid, with a Self-Sufficiency (SS) rate of 12.51%. Expanding PV capacity by 200 kWp increased SS to 32.1% but generated significant energy surpluses. The optimal configuration, integrating a 600 kWh BESS, increased SS to 37.3% while restoring the Self-Consumption (SC) rate to 99.8%. Furthermore, the integrated N-HiTS predictive model achieved a coefficient of determination of 0.64 under highly variable weather conditions. Ultimately, the results demonstrate the critical value of combining empirical simulation, optimized storage sizing, and advanced forecasting techniques to support robust REC planning.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/a19080690first seen 2026-09-06 05:04:37
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