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Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support

Author

Listed:
  • Cai, Helen
  • Zhang, Wanhao
  • Yuan, Qiong
  • Salameh, Anas A.
  • Alahmari, Saad
  • Ferrara, Massimiliano

Abstract

Enhancing cost-effective energy management in buildings is critical for achieving sustainability goals and addressing the challenges posed by rising energy use, which is a major concern for energy policy frameworks worldwide. This study is a trailblazer in using multi-criteria decision-making (MCDM) methodologies for the real-time operational optimisation of building energy systems. Data collection and pre-processing, feature extraction, feature selection, classification, trust authentication, encryption, and decryption are among the techniques used in this approach. Pre-processing procedures for the raw data include feature encoding, dimension reduction, and normalisation approaches. The Hybrid Grey Level Co-occurrence Matrix Fast Fourier Transform (HGLCM-FFT) method is used for feature extraction. Filter-based methods are used for feature selection, including IG, CS, symmetric uncertainty, and gain ratio. The Hierarchical Gradient Boosted Isolation Forest (HGB-IF) technique is used for the classification. Distributed Adaptive Trust-Based Authentication (DAT-BA), a security architecture in distributed cloud environments, uses trust authentication. The Particle Swarm Optimized Symmetrical Blowfish (PSOSB) method is used for encryption and decryption.The proposed framework not only ensures robust data security but also provides actionable insights for energy efficiency improvements, aligning with broader economic and environmental objectives. The suggested work is implemented using OS Python – 3.9.6; the performance of the proposed model is Attack Detection Rate, False alarm rate, True positive rate, Network usage, CPU usage, Encryption time, encryption time, and Throughput.

Suggested Citation

  • Cai, Helen & Zhang, Wanhao & Yuan, Qiong & Salameh, Anas A. & Alahmari, Saad & Ferrara, Massimiliano, 2025. "Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support," Energy Economics, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:eneeco:v:142:y:2025:i:c:s0140988325000076
    DOI: 10.1016/j.eneco.2025.108184
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    References listed on IDEAS

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    1. Zhang, Shangfeng & Zhang, Chaojie & Su, Zitian & Zhu, Mengyue & Ren, Huiru, 2023. "New structural economic growth model and labor income share," Journal of Business Research, Elsevier, vol. 160(C).
    2. Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Chen, Tengpeng & Cao, Yuhao & Qing, Xinlin & Zhang, Jingrui & Sun, Yuhao & Amaratunga, Gehan A.J., 2022. "Multi-energy microgrid robust energy management with a novel decision-making strategy," Energy, Elsevier, vol. 239(PA).
    4. Feng, Ye & Chen, Jinglong & Luo, Ji, 2024. "Life cycle cost analysis of power generation from underground coal gasification with carbon capture and storage (CCS) to measure the economic feasibility," Resources Policy, Elsevier, vol. 92(C).
    5. Zhang, Shangfeng & Li, Xiujie & Zhang, Chaojie & Luo, Jiayu & Cheng, Can & Ge, Wanjun, 2023. "Measurement of factor mismatch in industrial enterprises with labor skills heterogeneity," Journal of Business Research, Elsevier, vol. 158(C).
    6. Mobin Zarreh & Saeed Yaghoubi & Hamideh Bahrami, 2024. "Pricing of Drinking Water under Dynamic Supply and Demand based on Government Role: A Game-Theoretic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2101-2133, April.
    7. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    8. Mobin Zarreh & Mohammad Khandan & Alireza Goli & Adel Aazami & Sebastian Kummer, 2024. "Integrating Perishables into Closed-Loop Supply Chains: A Comprehensive Review," Sustainability, MDPI, vol. 16(15), pages 1-45, August.
    9. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    10. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
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