Open pz-max opened 2 months ago
Add historical forecast data (e.g. day-ahead) to quantify forecast errors for renewables
Economic dispatch optimizations for different energy market regimes (intraday, day-ahead) require historical forecast data. When available, it is also possible to quantify typical forecast error e.g. by comparing predicted (e.g. day-ahead) vs actual observed/estimated data. An example of how this could look is given by the DWD who quantifies the day-ahead vs actual observed forecast error with 15min resolution: https://www.dwd.de/EN/research/weatherforecasting/num_modelling/07_weather_forecasts_renewable_energy/weather_forecasts_renewable_energy_node.html
Possible API/ inspiration:
I have already done something similiar and will push it up until mid of november., needs only some fixing due to the last update in atlite. Best, Tim
Detailed Description
Add historical forecast data (e.g. day-ahead) to quantify forecast errors for renewables
Context
Economic dispatch optimizations for different energy market regimes (intraday, day-ahead) require historical forecast data. When available, it is also possible to quantify typical forecast error e.g. by comparing predicted (e.g. day-ahead) vs actual observed/estimated data. An example of how this could look is given by the DWD who quantifies the day-ahead vs actual observed forecast error with 15min resolution: https://www.dwd.de/EN/research/weatherforecasting/num_modelling/07_weather_forecasts_renewable_energy/weather_forecasts_renewable_energy_node.html
Possible Implementation
Possible API/ inspiration: