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[{"model": "core.projectfund", "pk": 30751, "fields": {"project": 7977, "organisation": 4, "amount": 299191, "start_date": "2022-06-30", "end_date": "2023-03-30", "raw_data": 41855}}]
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[{"model": "core.projectorganisation", "pk": 87677, "fields": {"project": 7977, "organisation": 9785, "role": "PARTICIPANT_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 87676, "fields": {"project": 7977, "organisation": 9785, "role": "LEAD_ORG"}}]
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| Jan. 28, 2023, 11:09 a.m. |
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[{"model": "core.projectperson", "pk": 54750, "fields": {"project": 7977, "person": 11000, "role": "PM_PER"}}]
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| Jan. 28, 2023, 10:51 a.m. |
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{"title": ["", "State-of-the-art solar PV generation forecast for individual PV systems"], "description": ["", "\nWith SBRI support Open Climate Fix (OCF) will develop an open-source state-of-the-art solar PV generation forecast for individual PV systems. The forecast will help our first users to optimise and better manage their solar energy, storage or distributed energy assets, which reduces CO2 emissions and costs for all energy users. Additionally, with the expansion of solar, it could be used at the individual household level in the near future.\n\nAs shown in our market research, there is a clear need for more accurate PV solar generation forecasts. Most of the forecasts currently available rely solely on numerical weather predictions (NWPs), do not make use of all the data available and struggle to forecast short time horizons ahead, which are important when making operational decisions on assets.\n\nHow is our forecast different? Our near-term forecasting solutions incorporate cutting-edge machine learning (ML) research combining a range of available data sources such as five-minutely satellite imagery, NWPs, and PV data from thousands of individual PV systems. Satellite data provides weather information that is only five minutes old as opposed to hours, as in the case of NWPs. Currently, meteorologists analyse satellite imagery, but most small companies do not have the resources to hire these skills. Satellite imagery has not yet been widely used in traditional solar PV forecasting methodologies, thus we are particularly interested in incorporating it to improve our models.\n\nOCF is a non-profit product lab, fully focused on reducing CO2 emissions. Every part of the organisation is designed to maximise climate impact, such as our open and collaborative approach, our rapid prototyping, and our attention on finding scalable & practical solutions.\n\nOCF applies open-source principles stewarded by the Open Source Initiative (OSI) and an MIT licence to all our solutions. We will apply the same open-source approach to the SBRI project. We will make all the code openly available for other researchers to contribute to and learn from via GitHub. We are working on implementing current literature at the cutting edge of machine learning from Google Research and DeepMind.\n\n \nAll our work to date is available on [GitHub][0]. With a team with deep experience in leading technology firms and machine learning, we bring the latest collaborative approaches and combine them with many years' experience in the energy industry. Through this mission-driven approach, we have already attracted significant interest and established a community of contributors around our work.\n\n[0]: https://github.com/openclimatefix\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Active"]}
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| Jan. 28, 2023, 10:51 a.m. |
Added
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{"external_links": [32791]}
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| Jan. 28, 2023, 10:51 a.m. |
Created
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[{"model": "core.project", "pk": 7977, "fields": {"owner": null, "is_locked": false, "coped_id": "5d4e331e-268b-4cc7-a86d-cfcc9799e04a", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 41842, "created": "2023-01-28T10:33:27.695Z", "modified": "2023-01-28T10:33:27.695Z", "external_links": []}}]
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