Implementasi Algoritma Machine Learning untuk Prediksi Beban Listrik Harian di Wilayah Perkotaan

Abstract

Electric load prediction is crucial in urban energy management. This study develops a machine learning model to predict daily electricity consumption based on historical data and external factors, such as temperature and humidity. The algorithms used include Random Forest, K-Nearest Neighbor, and Recurrent Neural Network. The resulting model shows high prediction accuracy and can be implemented in modern grid systems.