Authors - Joven A. Tolentino Abstract - The growing demand for electricity necessitates effective monitoring and forecasting of consumption trends. This study employs ARIMA modeling, using data from the Department of Energy, Philippines, to analyze and predict electricity consumption. The forecast for the next two years indicated an 18.99% increase in consumption between 2016 and 2017.To enhance analysis, the predicted data was clustered using the K-Means algorithm to group months with similar consumption patterns. This approach identified periods of high, medium, and low electricity usage, providing valuable insights into peak demand months. Such data-driven findings can guide electricity providers in prioritizing resources and implementing strategies to address fluctuations in consumer demand effectively. This study emphasizes the importance of forecasting and clustering as tools for decision-making to mitigate challenges arising from increasing electricity demand.
Open Zoom
