TY  - JOUR
IS  - 3
KW  - Convolutional neural network; CNN; load forecasting; solar home system; SHS; energy access
ID  - discovery10142552
AV  - public
N2  - 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.
JF  - Energies
A1  - Kizilcec, V
A1  - Spataru, C
A1  - Lipani, A
A1  - Parikh, P
VL  - 15
UR  - https://doi.org/10.3390/en15030857
Y1  - 2022/02/01/
TI  - Forecasting Solar Home System Customers? Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access
PB  - MDPI AG
N1  - This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
ER  -