<![CDATA[Solar energy is a key renewable resource, particularly valuable in tropical regions like Bali, where sunlight is consistently available throughout the year. Accurate estimation of sunshine duration is essential for assessing solar energy potential, as it directly affects photovoltaic (PV) system performace and informs strategic planning for renewable energy development. This study aims to develop a spatiotemporal statistical interpolation model to estimate and predict sunshine duration patterns across Bali, thereby enhancing the planning and deployment of solar energy infrastructure. This quantitative research applies space-time kriging with local drift using sunshine duration data (in hours) collected from four meteorological stations between 2019 and 2023. The method effectively captures spatial and temporal dependencies by integrating local drift as a deterministic trend component. Among several models tested, the Gaussian-Gaussian-Gaussian (Gau-Gau-Gau) combination delivered the best performance, with an RMSE of 2.3085. The results show a clear seasonal cycle, with higher sunshine duration during the dry season (May–October) and lower values in the wet season (November–March). Northern and eastern Bali, particularly Buleleng and Karangasem, demonstrate the highest solar potential, while central mountainous areas show lower sunshine exposure due to cloud coverage. These results offer not only a methodological contribution through the application of spatiotemporal kriging with local drift, but also a practical framework for decision-makers. The insights can guide strategic placement of solar farms, optimize energy yield forecasts, and support resilient infrastructure planning in line with Bali’s climatic realities and energy needs.]]>
