<![CDATA[Rooftop photovoltaic (PV) systems are increasingly deployed in tropical regions to support renewable energy adoption. However, reliable PV performance assessment typically requires long-term field measurements, which are often unavailable due to limited monitoring infrastructure and operational constraints. This study proposes a hybrid measured-synthetic framework for generating representative PV performance profiles and predicting energy production using limited field observations combined with meteorological data. The main contribution of this work is the development of a practical framework that extends sparse PV measurements into daily, monthly, and annual performance estimates. The proposed framework integrates measured PV operating parameters, including module temperature, voltage, current, and wind speed, collected from a rooftop PV installation in Batam, Indonesia, with solar radiation and ambient temperature data obtained from the NASA POWER database. Due to the limited availability of field measurements, a synthetic hourly dataset was generated using actual observations collected on 15 October 2022 in conjunction with photovoltaic performance modeling and meteorological information. The generated dataset was subsequently used to reconstruct representative daily operating profiles and estimate energy production over longer time horizons. The results indicate that the proposed framework successfully generated physically consistent PV performance profiles under tropical climatic conditions. The framework estimated a daily energy production of 34.27 kWh/day and an annual energy production of approximately 12,505.5 kWh/year. Validation against the available field measurements produced Mean Absolute Percentage Error (MAPE) values of 0.08%, 6.80%, and 7.03% for voltage, current, and power, respectively, indicating satisfactory agreement between measured and synthetic datasets. The proposed framework reduces dependence on extensive monitoring campaigns while preserving representative operational characteristics of photovoltaic systems. Therefore, it can serve as an effective tool for preliminary PV performance assessment and energy forecasting in regions where long-term measurement data are unavailable. Future work should incorporate longer monitoring periods, direct irradiance measurements, and broader validation datasets to improve prediction accuracy and generalizability.]]>
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