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Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access

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  • Vivien Kizilcec

    (Engineering for International Development Research Centre, Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK)

  • Catalina Spataru

    (UCL Energy Institute, The Bartlett School of Environment, Energy & Resources, University College London, London WC1H 0NN, UK)

  • Aldo Lipani

    (UCL Energy Institute, The Bartlett School of Environment, Energy & Resources, University College London, London WC1H 0NN, UK)

  • Priti Parikh

    (Engineering for International Development Research Centre, The Bartlett School of Sustainable Construction, University College London, London WC1E 6BT, UK)

Abstract

Off-grid technologies, such as solar home systems (SHS), offer the opportunity to alleviate global energy poverty, providing a cost-effective alternative to an electricity grid connection. However, there is a paucity of high-quality SHS electricity usage data and thus a limited understanding of consumers’ past and future usage patterns. This study addresses this gap by providing a rare large-scale analysis of real-time energy consumption data for SHS customers ( n = 63,299) in Rwanda. Our results show that 70% of SHS users’ electricity usage decreased a year after their SHS was installed. This paper is novel in its application of a three-dimensional convolutional neural network (CNN) architecture for electricity load forecasting using time series data. It also marks the first time a CNN was used to predict SHS customers’ electricity consumption. The model forecasts individual households’ usage 24 h and seven days ahead, as well as an average week across the next three months. The last scenario derived the best performance with a mean squared error of 0.369. SHS companies could use these predictions to offer a tailored service to customers, including providing feedback information on their likely future usage and expenditure. The CNN could also aid load balancing for SHS based microgrids.

Suggested Citation

  • Vivien Kizilcec & Catalina Spataru & Aldo Lipani & Priti Parikh, 2022. "Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access," Energies, MDPI, vol. 15(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:857-:d:732931
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    References listed on IDEAS

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    1. Narayan, Nishant & Chamseddine, Ali & Vega-Garita, Victor & Qin, Zian & Popovic-Gerber, Jelena & Bauer, Pavol & Zeman, Miroslav, 2019. "Exploring the boundaries of Solar Home Systems (SHS) for off-grid electrification: Optimal SHS sizing for the multi-tier framework for household electricity access," Applied Energy, Elsevier, vol. 240(C), pages 907-917.
    2. Opoku, Richard & Obeng, George Y. & Adjei, Eunice A. & Davis, Francis & Akuffo, Fred O., 2020. "Integrated system efficiency in reducing redundancy and promoting residential renewable energy in countries without net-metering: A case study of a SHS in Ghana," Renewable Energy, Elsevier, vol. 155(C), pages 65-78.
    3. Gustavsson, Mathias, 2007. "With time comes increased loads—An analysis of solar home system use in Lundazi, Zambia," Renewable Energy, Elsevier, vol. 32(5), pages 796-813.
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