SHORT-TERM ELECTRICAL LOAD FORECASTING USING A HYBRID LEVENBERG-MARQUARDT AND CUCKOO SEARCH ALGORITHM

Abstract

The Nigerian power system continues to experience epileptic, inadequate, and unreliable electricity supply, largely due to the continuous increase in load from a growing consumer base. Accurate short-term load forecasting is essential to ensure efficient planning, reliable operations, and sustainable development of the power system. This study emphasizes the critical role of load forecasting in anticipating demand and mitigating operational challenges in the Nigerian power sector. By implementing advanced forecasting techniques, system planners can optimize power generation, reduce outages, and enhance service reliability. The research underscores the need for robust forecasting models that account for dynamic changes in electricity demand, providing insights for both immediate and long-term power system planning. The findings highlight that improving forecasting accuracy is pivotal in strengthening the reliability and performance of the Nigerian power system.