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

Vivien Kizilcec, Catalina Spataru, Aldo Lipani and Priti Parikh
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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

Energies, 2022, vol. 15, issue 3, 1-25

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.

Keywords: convolutional neural network; CNN; load forecasting; solar home system; SHS; energy access (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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