This repository hosts code for time series forecasting using the Prophet library.
How to use:
python3 src/main.py
timestamp: The time of the observation as a UTC+0 string (National Energy Market time is always UTC+10 so you may need to shift these 10 hours forward for the times to align with the australian day/night cycle).price: the per megawatt spot price of electricity in South Australia for that interval in AUD (courtesy of opennem).demand: the total demand for electricity in South Australia for that interval in megawatts. Note: because of the high penetration of rooftop solar in south australia, the demand usually reaches a minimum at around midday UTC+10 (courtesy of opennem).temp_air: the air temperature in Adelaide at that time in degrees celsius (courtesy of opennem).pv_power: the total power in kilowatts generated by the solar panel attached to the battery at that time (courtesy of solcast).pv_power_forecast_1h: the total power in kilowatts that the solar panel is forecasted to generate in 1 hour from that time (courtesy of solcast).pv_power_forecast_2h: the total power in kilowatts that the solar panel is forecasted to generate in 2 hours from that time (courtesy of solcast).pv_power_forecast_24h: the total power in kilowatts that the solar panel is forecasted to generate in 24 hours from that time (courtesy of solcast).pv_power_basic: an estimate of the total solar power in kilowatts generated by the south australian energy grid at that time (courtesy of solcast).
Note: all solcast data is only provided for 1 year, so training_data.csv has NaN values for all solcast data for the first 2 years.