COMPARATIVE ANALYSIS OF SOLAR ENERGY PROGNOSIS TOOLS IN DIVERSE CLIMATIC CONDITIONS: A CASE STUDY OF GERMANY AND INDONESIA

Abstract

As renewable energy adoption grows, solar photovoltaic (PV) systems play a key role in electricity generation. However, their weather-dependent output challenges grid reliability, making accurate solar forecasting crucial, especially in regions with diverse climates. The purpose of the study is to evaluate the accuracy and reliability of prognosis models in predicting PV power output under different weather conditions. Forecast and historical meteorological data were obtained from Meteoblue and used as inputs in PVsyst simulations, which were then validated against actual power generation data from PV systems in both countries. Key performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were calculated to assess forecasting precision. Germany’s forecasts were closely aligned with actual outputs, while Indonesia's results exhibited greater variability and larger error margins. The results revealed that prognosis tools perform more accurately in temperate climates, as demonstrated by the German case, which showed lower error rates across all metrics. In contrast, the tropical climate of Indonesia posed challenges for forecast reliability, primarily due to high variability in cloud cover and the impact of urban pollution. Nonetheless, the forecast model exhibited improvement in stable tropical conditions. This study highlights the critical importance of localized calibration and environmental factors in solar forecasting and emphasizes the need for enhanced modeling approaches in diverse climate zones.