DIGITAL TWINS IN BUILDING ENERGY MANAGEMENT FOR ELECTRICITY CONSUMPTION FORECASTING AND ENERGY EFFICIENCY IMPROVEMENT
建物エネルギー管理におけるデジタルツイン：電力消費予測とエネルギー効率改善 (AI 翻訳)
Denys SHPAK
🤖 gxceed AI 要約
日本語
本論文は、デジタルツインを建物エネルギー管理に活用し、電力消費予測とエネルギー効率向上を図る手法を検討。BIM/IoTデータと機械学習を統合した閉ループ制御フレームワークを提案し、計測・検証、モデルドリフト、相互運用性、サイバーセキュリティなどの実装上の課題を整理。予測モデルとシナリオ分析に基づくプロアクティブ制御への移行可能性を示す。
English
This paper examines digital twins for building energy management, focusing on electricity consumption forecasting and energy efficiency. It proposes a closed-loop framework integrating BIM, IoT, and machine learning, addressing measurement & verification, model drift, interoperability, and cybersecurity. The study demonstrates potential for proactive control based on predictive models and scenario analysis.
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ZEB（ネット・ゼロ・エネルギー・ビル）推進や省エネ法改正により、建物のエネルギー管理高度化が急務。本論文のデジタルツイン手法は、BELS認証やRE100達成にも寄与し得る。
In the global GX context
Globally, digital twins align with smart building initiatives and net-zero targets. The framework offers practical guidance for integrating IoT and ML into BMS/EMS, relevant for LEED, BREEAM, and ISO 50001 compliance.
👥 読者別の含意
🔬研究者:Provides a structured methodology and identifies key limitations for digital twin deployment in buildings.
🏢実務担当者:Offers a practical implementation cycle for energy managers to adopt digital twins for forecasting and efficiency.
🏛政策担当者:Highlights the potential of digital twins to support building energy codes and smart grid integration.
📄 Abstract（原文）
The article examines the application of digital twins as a tool for modernizing building energy management aimed at electricity consumption forecasting and improving energy efficiency. Based on a review of contemporary approaches, the study analyzes how the integration of BIM/semantic models, IoT data, and operational systems with machine learning methods enables the implementation of a closed-loop control framework. Methods for electricity consumption forecasting are systematized according to time horizons and their suitability for operational tasks. Typical scenarios for improving energy efficiency through the use of digital twins are presented. A methodological framework for the implementation of digital twins in buildings is proposed, taking into account measurement and verification, model drift, interoperability, and cybersecurity. The impact of digital twins on the quality of managerial decision-making is identified, particularly in enhancing energy consumption controllability and enabling a transition to proactive control of building engineering systems based on predictive models and scenario analysis, as well as in creating conditions for the development of more flexible and sustainable building energy systems. The practical implementation cycle of digital twins in building energy management is generalized. Key limitations of digital twin deployment in buildings are outlined. The dependence of digital twin effectiveness on data consistency and the correctness of energy management problem formulation is demonstrated. Keywords: digital twin, building, electricity consumption forecasting, energy efficiency, IoT, BMS/EMS, machine learning, optimization.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.15407/srenergy2026.02.100first seen 2026-06-21 05:04:40
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