Back to Search
Start Over
Forecasting Solar Home System Customers' Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access.
- Source :
- Energies (19961073); Feb2022, Vol. 15 Issue 3, p857-N.PAG, 1p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 15
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 155244142
- Full Text :
- https://doi.org/10.3390/en15030857