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A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

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  • Bhavsar, S.
  • Pitchumani, R.

Abstract

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

Suggested Citation

  • Bhavsar, S. & Pitchumani, R., 2021. "A novel machine learning based identification of potential adopter of rooftop solar photovoltaics," Applied Energy, Elsevier, vol. 286(C).
  • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s0306261921000611
    DOI: 10.1016/j.apenergy.2021.116503
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    References listed on IDEAS

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    1. Faiers, Adam & Neame, Charles, 2006. "Consumer attitudes towards domestic solar power systems," Energy Policy, Elsevier, vol. 34(14), pages 1797-1806, September.
    2. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2013. "Motivations and barriers associated with adopting microgeneration energy technologies in the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 655-666.
    3. Aklin, M. & Bayer, P. & Harish, S.P. & Urpelainen, J., 2018. "Economics of household technology adoption in developing countries: Evidence from solar technology adoption in rural India," Energy Economics, Elsevier, vol. 72(C), pages 35-46.
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    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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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