This project was developed from Center for Artificial Intelligence at KKU, Link.
The power generation data extracted from the polycrystalline PV systems placed at KKU are associated with four primary data sources measured over the same period of time. Weather station sensors (WS) were located near the station to measure various parameters, namely ambient temperature (Ta), relative humidity (RH), wind speed (W), wind direction (WD), solar irradiation (SR), and precipitation (R), where solar irradiance was found to be more accurate using the Py sensor. The computed parameters from the WS and Py were also considered. The latter included the solar PV system inverters (N) and panel sensors (PVSR). The four sources of data were utilized together to conduct our experiment. However, the collected data were for December 2019 until February 2020, between the autumn and the winter seasons. During this time, data were acquired and tabulated from sunrise to sunset at an interval of each five minutes for the parameters of low and high temperatures, average temperature, humidity, wind speed, and solar radiations. This differentiated cloudy days, clear-sky days, and mix days.
- Data collection.
- Feature selection
- Feature extraction
- Train DL & ML models to Predict the PV power.
- Model evaluation.
- Journal submission publication.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.