solar_panel_model

Training a polynomial regression model to predict solar panel intensity based on local weather data.

See the full tour here: https://github.com/Tareq62/solar_panel_model/blob/master/solar_polynomial_regression.ipynb

Weather data inputs:

• Temperature
• Hours of daylight
• Humidity
• Precipitation
• Theoretical solar intensity, depending on:
  • Latitude
  • Longitude
  • Day of year
  • Time of day (5-minute resolution)

Output:

• Predicted solar intensity (MW)
• Error score compared to actual solar panel data

Assumptions based on NOAA input data:

• Temperature is in Celsius * 1000, parsed as first 3- or 4-digit number ending in "00"
• Visibility is parsed as a number followed by "SM"
• Humidity is parsed as a 1- or 2-digit number representing relative humidity in percentage

Sources

NOAA weather data files

ftp://ftp.ncdc.noaa.gov/pub/data/asos-fivemin/6401-2006

Solar panel performance data

https://www.nrel.gov/grid/solar-power-data.html