Solar power generation analysis and forecasting real-world data using LSTM and autoregressive CNN

tosun n., sert e., AYAZ E. , YILMAZ e., GÖL M.

3rd International Conference on Smart Energy Systems and Technologies, SEST 2020, Virtual, Istanbul, Turkey, 7 - 09 September 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/sest48500.2020.9203124
  • City: Virtual, Istanbul
  • Country: Turkey
  • Keywords: Artificial neural networks, Autoregressive convolutional neural networks, Long-short term memory, Solar power forecasting


© 2020 IEEE.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.