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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In recent years, the share of solar power in total energy production has gained a rapid increase. Therefore, prediction of solar power production has become increasingly important for energy regulations. In this study we proposed an ensemble method which gives competitive prediction performance for solar power production. This method firstly decomposes the nonlinear power production data into components with a multi-scale decomposition technique such as Empirical Mode Decomposition (EMD). These components a...
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In this study, a novel metamaterial absorber (MA) is designed and numerically demonstrated for solar energy harvesting. The structure is composed of three layers with different thicknesses. The interactions of three layers bring about the plasmonic resonances. Although the main operation frequency of the structure is chosen between 430 and 770 THz, which is the visible light regime, the proposed structure is also investigated in the ultra-violet (UV) region. One can see from the results that the proposed st...
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: