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<p>As the world shifts towards sustainable energy solutions, the integration of Building Information Modeling (BIM) with Big Data Analytics has emerged as a transformative approach for optimizing Renewable Energy Systems (RES) in power energy facilities and buildings. Since in exisitng Systems facing the fluctuations of renewable electricity sources, inadequate energy consumption and inefficient integration of ESS that causes unforeseen grid fluctuations and high operating expenses. Hence in this proposed work, recommends an Integrated Design Decision Support System (IDDSS) which integrates BIM for Real-Time facility modeling and Big Data analytics for improvement of decision making. Using Graph Neural Networks (GNNs), the system contemplates the multifaceted connections between energy components to accurately predict energy demand, renewable energy generation, and perform predictive maintenance. Consequently, the system is given performance indications such as Energy Efficiency Ratio (EER) of 0.85, Load Factor (LF) of 75%, and Power Loss Reduction (PLR) of 20%, which points to a more efficient system and guaranteed stability. Furthermore, the variable Renewable Energy Penetration (REP) is tuned to 60% and the Grid Reliability (Availability Factor) achieves 99. 5% which corroborates the system capacity to increase both sustainability and efficiency.</p>

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Integrated Design Decision Support System for Power Energy Facilities and Buildings Based on BIM and Big Data Analysis

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Pei Yang,
  • Cungang Liu

摘要

As the world shifts towards sustainable energy solutions, the integration of Building Information Modeling (BIM) with Big Data Analytics has emerged as a transformative approach for optimizing Renewable Energy Systems (RES) in power energy facilities and buildings. Since in exisitng Systems facing the fluctuations of renewable electricity sources, inadequate energy consumption and inefficient integration of ESS that causes unforeseen grid fluctuations and high operating expenses. Hence in this proposed work, recommends an Integrated Design Decision Support System (IDDSS) which integrates BIM for Real-Time facility modeling and Big Data analytics for improvement of decision making. Using Graph Neural Networks (GNNs), the system contemplates the multifaceted connections between energy components to accurately predict energy demand, renewable energy generation, and perform predictive maintenance. Consequently, the system is given performance indications such as Energy Efficiency Ratio (EER) of 0.85, Load Factor (LF) of 75%, and Power Loss Reduction (PLR) of 20%, which points to a more efficient system and guaranteed stability. Furthermore, the variable Renewable Energy Penetration (REP) is tuned to 60% and the Grid Reliability (Availability Factor) achieves 99. 5% which corroborates the system capacity to increase both sustainability and efficiency.