Forecasting Ambient Air Temperature for Solar Photovoltaic Panel Predictive Maintenance in Kuala Kangsar, Perak, Malaysia

This study presents a forecasting approach to predict ambient air temperatures for the predictive maintenance of solar photovoltaic (PV) panels in Kuala Kangsar, Perak, Malaysia. Accurate temperature forecasting is critical as ambient air temperature significantly influences solar panel efficiency, performance, and maintenance requirements. A comprehensive dataset spanning 19 years (2005–2023) of hourly solar and weather variables, obtained from the Photovoltaic Geographical Information System (PVGIS), was analysed using advanced smoothing techniques, including exponential smoothing models. The study identified the Damped-Trend Linear Exponential Smoothing model as the most effective method based on Akaike and Bayesian Information Criteria. Forecast results for 2024 demonstrated good predictive accuracy, aiding the optimisation of maintenance schedules and the performance of solar PV systems. The findings underscore the importance of integrating advanced predictive techniques to enhance the sustainability and reliability of renewable energy projects.