Net Zero Energy cost Building system design based on Artificial Intelligence
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DOI: 10.1016/j.apenergy.2023.122348
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Cited by:
- 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).
- 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.
- 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.
- 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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Keywords
Net Zero Energy cost Building (nZEcB); Distributed generation; Renewable energy; Energy planning; Artificial Intelligence;All these keywords.
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