Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN

tosun, nail
sert, egemen
Ayaz, Enes
YILMAZ, ekin
Generated power of a solar panel is volatile and susceptible to environmental conditions. In this study, we have analyzed variables affecting the generated power of a 17.5 kW real-world solar power plant with respect to five independent variables over the generated power: irradiance, time of measurement, panel's temperature, ambient temperature and cloudiness of the weather at the time of measurement. After our analysis, we have trained three different models to predict intra-day solar power forecasts of the plant. Our models are able to predict future power output of the solar power plant with less than 10% RMSE without requiring additional sensor data, e.g. a camera to observe clouds. Based on our forecasting accuracy, our study promises: fast, scaleable and effective solutions to solar power plant maintainers and may facilitate grid safety on a large scale.


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Citation Formats
n. tosun, e. sert, E. Ayaz, e. YILMAZ, and M. GÖL, “Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN,” 2020, Accessed: 00, 2021. [Online]. Available: