Time Series Analysis for Renewable Energy Forecasting in the Philippines: Enhancing the Reliability and Efficiency of Intermittent Energy Sources

The Philippines is rapidly expanding its renewable energy (RE) capacity to meet ambitious targets of 35% RE in the power generation mix by 2030 and 50% by 2040. However, the increasing penetration of intermittent sources like solar and wind poses significant challenges to grid stability and operational efficiency, particularly in an archipelagic nation prone to extreme weather. This paper reviews the application of time series analysis for RE forecasting in the Philippine context. It examines key methodologies, including ARIMA, SARIMA, Prophet, and LSTM models, alongside hybrid approaches, and discusses their suitability for predicting variable RE generation. The paper synthesizes findings from local studies, highlighting the impacts of forecasting on grid management and the specific challenges related to data availability and quality. Accurate RE forecasting is identified as a critical enabler for enhancing power system reliability, optimizing resource dispatch, and supporting the Philippines' transition towards a sustainable energy future. Collaborative efforts in data infrastructure development, localized model refinement, and adaptive forecasting strategies for events like typhoons are crucial for maximizing the benefits of the nation's abundant renewable resources.