Add historical forecast data e.g. day-ahead
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- Dominant language
- Python
- Stars
- 402
- Forks
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Description
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:
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named. Start by reviewing atlite's existing data-source and time-series interfaces, then examine the proposed Open-Meteo, Herbie, and OpenWeatherMap APIs. Done should mean that historical forecast data, such as day-ahead renewable forecasts, can be accessed and compared with observed or estimated data to quantify forecast error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100