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Net Zero Energy cost Building system design based on Artificial Intelligence

Author

Listed:
  • Rocha, Helder R.O.
  • Fiorotti, Rodrigo
  • Louzada, Danilo M.
  • Silvestre, Leonardo J.
  • Celeste, Wanderley C.
  • Silva, Jair A.L.

Abstract

This paper presents a solution to the energy planning problem in buildings by implementing the net Zero Energy cost Building (nZEcB), a new concept that refers to buildings with zero or almost zero annual energy costs, using Artificial Intelligence (AI) techniques such as bidirectional long short-term memory, ordinary least squares linear regression, K-means, Pearson’s correlation, decision tree, and binary gravitational search algorithm. AI techniques are used to design the optimal structure of a distributed generation system. The system includes wind and photovoltaic renewable energy sources, a battery bank, and an automated capacitor bank for power factor compensation. A case study was conducted in a real public building with an annual consumption of 1.748 GWh, which resulted in the specification of a distributed generation system with a generation of 2.805 GWh per year. This system meets approximately 160.5% of the building’s electrical demand and 99.999% of annual energy costs, i.e., attending the nZEcB concept. The excess production of approximately 60% of the energy is necessary because the exported energy (feed-in tariff) has a lower value than that imported from the network, having to deduct costs with demand, in addition to having to compensate for losses in the battery banks. The project has a payback period of 6.79 years. This novel study demonstrates the feasibility and effectiveness of using AI techniques to achieve nZEcBs more efficiently, economically and sustainably.

Suggested Citation

  • Rocha, Helder R.O. & Fiorotti, Rodrigo & Louzada, Danilo M. & Silvestre, Leonardo J. & Celeste, Wanderley C. & Silva, Jair A.L., 2024. "Net Zero Energy cost Building system design based on Artificial Intelligence," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923017129
    DOI: 10.1016/j.apenergy.2023.122348
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    References listed on IDEAS

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    Cited by:

    1. Kim, Dongsu & Seomun, Gu & Lee, Yongjun & Cho, Heejin & Chin, Kyungil & Kim, Min-Hwi, 2024. "Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning," Applied Energy, Elsevier, vol. 368(C).
    2. Giuseppe Piras & Francesco Muzi & Zahra Ziran, 2024. "Open Tool for Automated Development of Renewable Energy Communities: Artificial Intelligence and Machine Learning Techniques for Methodological Approach," Energies, MDPI, vol. 17(22), pages 1-16, November.
    3. Yushi Wang & Beining Hu & Xianhai Meng & Runjin Xiao, 2024. "A Comprehensive Review on Technologies for Achieving Zero-Energy Buildings," Sustainability, MDPI, vol. 16(24), pages 1-26, December.
    4. Fiorotti, Rodrigo & Fardin, Jussara F. & Rocha, Helder R.O. & Rua, David & Lopes, João Abel Peças, 2024. "Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems," Applied Energy, Elsevier, vol. 374(C).

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