Time-Series Forecasting of Solar Photovoltaic Power Generation in Malaysia

The need for accurate forecasting techniques to support the integration of solar power into the national grid is highlighted by Malaysia’s growing reliance on renewable energy. Using Global Horizontal Irradiance (GHI) data gathered in Melaka, this study proposes a time-series forecasting method for solar Photovoltaic (PV) power generation. Before analysis, a thorough dataset covering five years was pre-processed to guarantee accuracy, consistency, and dependability. In order to better predict PV output, the Auto-Regressive Integrated Moving Average (ARIMA) model was created and used to account for both short-term and long-term variations in solar irradiance. Comparisons between expected and observed values were used to evaluate the model’s performance, and the results showed how well it captured seasonal variations, diurnal cycles, and stochastic fluctuations related to tropical weather. The outcomes validate the ARIMA model’s applicability for Malaysian renewable energy forecasting, providing insightful information for improving grid stability, optimizing PV system performance, and guiding sustainable energy planning policy. In a broader sense, the results highlight how important sophisticated time-series models are for tackling the problems caused by weather variability in tropical areas, where precise solar energy forecasting is necessary for long-term energy security and effective resource management.