General Page Information
Item | Link/Context | Notes |
Project Name | OpenSTF | Exact spelling and capitalization |
Logo | Link to SVG | Provided by LFE Staff |
Hero image | Let us know what you imagine on your site, or link an image that you’d like us to use. Final choice will be a decision of the LF Energy staff. | |
Wiki | OpenSTF | Provided by LFE Staff, but built out by project |
Mailing List | Provided by LFE Staff | |
GitHub | https://github.com/alliander-opensource/openstf | |
Roadmap | ||
Metrics Dashboard | Provided by LFE Staff |
Project Overview (opening text)
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Openstf is a Python package which is used to make short term forecasts for the energy sector. This repository contains all components for the machine learning pipeline required to make a forecast. In order to use the package you need to provide your own data storage and retrieval interface. openstf is available at: https://pypi.org/project/openstf/
Project Details
The energy transition poses new challenges to all parties in the energy sector. For grid operators, the rise in renewable energy and electrification of energy consumption leads to the capacity of the grid to near its fysical constraints. Forecasting the load on the grid in the next hours to days is essential for anticipating on local congestion and making the most of existing assets.
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The Dutch DSO Alliander started the Short-Term-Forecasting project to anticipate congestion in the distribution grid, to allow for grid safety analysis in the transmission grid and to enable smart grid innovations to locally balance supply and demand within the constraints of the grid. By opensourcing the stack, the ambition is to provide an industry standard for generating and evaluating forecasts in the operational time-domain, as well as allow for structured collaboration.
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