A novel machine learning based identification of potential adopter of rooftop solar photovoltaics
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DOI: 10.1016/j.apenergy.2021.116503
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- Faiers, Adam & Neame, Charles, 2006. "Consumer attitudes towards domestic solar power systems," Energy Policy, Elsevier, vol. 34(14), pages 1797-1806, September.
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Cited by:
- Alipour, Mohammad & Taghikhah, Firouzeh & Irannezhad, Elnaz & Stewart, Rodney A. & Sahin, Oz, 2022. "How the decision to accept or reject PV affects the behaviour of residential battery system adopters," Applied Energy, Elsevier, vol. 318(C).
- Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
- Xie, Xiangmin & Peng, Fei & Zhang, Yan, 2022. "A data-driven probabilistic harmonic power flow approach in power distribution systems with PV generations," Applied Energy, Elsevier, vol. 321(C).
- Yasemin Ayaz Atalan & Abdulkadir Atalan, 2023. "Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
- Min, Yohan & Ko, Inhwan, 2023. "Causal effects of place, people, and process on rooftop solar adoption through Bayesian inference," Energy, Elsevier, vol. 285(C).
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Keywords
Rooftop solar adoption; Machine learning; Binary classification; Generative Adversarial Network; Bayesian optimization;All these keywords.
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