Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
Summary. This paper develops a load management system for off-grid solar power plants in rural areas using machine learning. The system combines support vector machines and fruit fly optimization to predict energy demand and detect anomalies in real time. Applied to a 50-household solar installation in Tanzania, the approach improves energy efficiency and utilization rates, offering a practical solution for sustainable rural electrification in Africa.
Cite this article
Wang, X., Rhee, H. S., & Ahn, S.. (2020). Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa. Applied Sciences. https://doi.org/10.3390/app10124171
Wang, Xinlin, et al. “Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa.” Applied Sciences, 2020. https://doi.org/10.3390/app10124171.
Wang, Xinlin, Herb S. Rhee, and Sung‐Hoon Ahn. 2020. “Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa.” Applied Sciences. https://doi.org/10.3390/app10124171.
@article{wang-2020-off-grid-power-plant-load,
title = {Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa},
author = {Xinlin Wang and Herb S. Rhee and Sung‐Hoon Ahn},
journal = {Applied Sciences},
year = {2020},
doi = {10.3390/app10124171},
url = {https://doi.org/10.3390/app10124171}
}
TY - JOUR TI - Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa AU - Xinlin Wang AU - Herb S. Rhee AU - Sung‐Hoon Ahn JO - Applied Sciences PY - 2020 DO - 10.3390/app10124171 UR - https://doi.org/10.3390/app10124171 ER -
Details
- DOI
- 10.3390/app10124171
- Countries
- Tanzania
- Regions
- Africa
- Categories
- energy, regional-innovation-systems
- Added
- 2026-04-28