Daily Electricity Load Forecasting in Ternate City Using ELM
Abstract
The continuously increasing growth of electricity demand necessitates accurate and systematic planning of electric power systems to ensure power flow quality and system reliability. Ternate City, as one of the major activity centers in North Maluku Province, has experienced a substantial rise in electricity consumption, thereby requiring an effective and reliable load forecasting approach. This study aims to predict the daily electricity load in Ternate City using the Extreme Learning Machine (ELM) method. The analysis is conducted using historical electricity load data, which are processed through data preprocessing stages, dataset partitioning into training and testing sets, and ELM-based modeling. The performance of the proposed model is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the MAPE values for the training dataset range from 5.84% to 13.63%, corresponding to very good to good performance categories. Meanwhile, the testing dataset yields MAPE values ranging from 13.45% to 33.09%, which fall within the good to sufficient performance categories. Furthermore, the prediction results are able to accurately capture daily electricity load fluctuation patterns from Monday to Sunday, including peak load periods. Based on these findings, the ELM method demonstrates strong potential as a reliable approach to support electric power system planning and to enhance the quality and reliability of electricity supply in Ternate City.
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