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

Date

2020-09-22

Author

tosun, nail
sert, egemen
Ayaz, Enes
YILMAZ, ekin
GÖL, MURAT

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

solar power forecasting, long-short term memory, artificial neural networks, autoregressive convolutional neural networks

URI

https://hdl.handle.net/11511/70226

DOI

https://doi.org/10.1109/sest48500.2020.9203124

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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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: https://hdl.handle.net/11511/70226.