Energy Reports (Nov 2023)
Real-time photovoltaic energy assessment using a GSM-based smart monitoring system: Addressing the impact of climate change on solar energy estimation software
Abstract
Climate change is altering weather patterns, leading to increased variability in cloud cover, precipitation, and temperature. These changing patterns make it more challenging to accurately forecast solar radiation levels, which directly impact solar energy generation. This study, evaluates the solar energy potential using a real-time GSM-based communication system combined with an off-grid PV power plant in Tehran and Ahvaz, two regions in Iran with contrasting climates. To measure the output voltage, C++ language programming was used in Arduino_IDE programming language was utilized along with an Arduino Nano board, ATmega328P microcontroller, and sensors for measuring current, voltage, temperature, humidity, and time. The recorded data was sent through short message service. The results were compared with simulations from PVsyst, PVGIS, and the Global Solar Atlas. The findings revealed that the PV power production potential in Tehran, which is characterized by a cold and wet climate, was 4.101 kwh.kwp −1 with a capacity factor of 17.09%. In contrast, Ahvas, with its warm and dry climate, had a potential of 3.271 kwh.kwp −1 and a capacity factor of 13.63%. These values differed significantly from the estimates provided by PVsyst and the Global Solar Atlas. The Global Solar Atlas overestimated the PV potential by 15% and 18.2% in Tehran and Ahvaz, respectively, while PVsyst underestimated it by more than 15% in both locations. The real-time energy production was measured at 246.1 Wh and 196.27 Wh, compared to the estimated values of 283 Wh and 232 Wh from the Global Solar Atlas, 178.36 Wh and 142.63 Wh from PVsyst, and 221.4 Wh and 196.9 Wh from PVGIS in Tehran and Ahvaz, respectively. These results underscore the importance of accounting for recent climate changes when predicting near real-time solar generation, particularly in remote areas and regions that deviate from global trends.
