Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function ()
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 hybrid 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 electric load
data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to
test the forecast model. The simulation results demonstrate the proposed
algorithm enhanced better forecasting accuracy.
Share and Cite:
Uwimana, E. , Zhou, Y. and Zhang, M. (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. doi:
10.4236/jpee.2023.118003.
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