Journal of Power and Energy Engineering, 2023, 11, 32-54 https://www.scirp.org/journal/jpee ISSN Online: 2327-5901 ISSN Print: 2327-588X DOI: 10.4236/jpee.2023.118003 Aug. 29, 2023 32 Journal of Power and Energy Engineering Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function Eustache Uwimana, Yatong Zhou, Minghui Zhang School of Electronics and Information Engineering, Beichen District, Hebei University of Technology, Tianjin, China Abstract In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hy- brid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical elec- tric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation re- sults demonstrate the proposed algorithm enhanced better forecasting ac- curacy. Keywords SVM, Quadratic SVM, Long-Term Electrical Load Forecasting, Residual Load Demand Series, Historical Electric Load How to cite this paper: Uwimana, E., Zhou, Y.T. and Zhang, M.H. (2023) Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function. Journal of Power and Energy Engineering, 11, 32-54. https://doi.org/10.4236/jpee.2023.118003 Received: July 21, 2023 Accepted: August 26, 2023 Published: August 29, 2023 Copyright © 2023 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access