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| Jan. 28, 2023, 11:08 a.m. |
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[{"model": "core.projectfund", "pk": 24589, "fields": {"project": 1776, "organisation": 4, "amount": 59911, "start_date": "2020-09-30", "end_date": "2020-12-31", "raw_data": 38702}}]
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| Jan. 28, 2023, 11:08 a.m. |
Created
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[{"model": "core.projectperson", "pk": 53803, "fields": {"project": 1776, "person": 11673, "role": "PM_PER"}}]
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| Jan. 28, 2023, 10:51 a.m. |
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{"external_links": []}
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectfund", "pk": 16692, "fields": {"project": 1776, "organisation": 4, "amount": 59911, "start_date": "2020-09-30", "end_date": "2020-12-31", "raw_data": 7638}}]
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| April 11, 2022, 3:45 a.m. |
Created
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[{"model": "core.projectorganisation", "pk": 62927, "fields": {"project": 1776, "organisation": 2148, "role": "PARTICIPANT_ORG"}}]
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| April 11, 2022, 3:45 a.m. |
Created
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[{"model": "core.projectorganisation", "pk": 62926, "fields": {"project": 1776, "organisation": 2148, "role": "LEAD_ORG"}}]
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| April 11, 2022, 3:45 a.m. |
Created
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[{"model": "core.projectperson", "pk": 38880, "fields": {"project": 1776, "person": 2547, "role": "PM_PER"}}]
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| April 11, 2022, 1:47 a.m. |
Updated
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{"title": ["", "Advanced AI for Integrated Financial Optimization of Wind Energy Assets"], "description": ["", "\nREOptimize Systems (REOS), was formed to exploit research developed at University of Edinburgh, which has implemented a unique approach to increasing the efficiency of wind turbines. Through advanced modelling and the application of novel machine learning techniques the algorithms minimise the end-to-end losses in the system. This technique has patents pending and is the result of 7 years of research at The University of Edinburgh. The success of the algorithms has been proven experimentally in small-scale wind turbines, and found to yield increases in energy capture of 6%. A 6% increase in energy capture can drive net profit increases for the operator on the order of 50%-100% depending on the specific turbine and location. If only half of UK turbines achieved a 6% gain, it would result in an additional 3000 GWhr of generation and a saving of 1.3 million tonnes of carbon in a single year. This is equivalent to removing around 290,000 petrol passenger cars from the streets. However, this 6% gain has been proven only in medium-scale wind at approximately 100 kW ratings. It is expected that larger turbines will start from a position of better control which will allow us to achieve gains on the order of 3%. REOS is currently preparing a pilot project to validate the technique on a MW scale Siemens 2.3-92 turbine, which is a workhorse of the UK onshore fleet. Now, through this new project, REOS will develop and integrate novel machine learning technologies into a single platform which will provide end-to-end financial optimization of wind power assets, with a truly holistic view of the entire wind system. The project will develop and integrate: * Continuous per-turbine settings optimization * Advanced detection of false alarms to increase in-service time * Advanced AI-based wind farm control This will create a step-change in the control and performance of wind energy assets with the aim of maintaining the gain of 6% increase in energy output in large modern wind farms. This will contribute to creating sustainable innovation and help deliver the transition to net-zero.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| April 11, 2022, 1:47 a.m. |
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{"external_links": [6451]}
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| April 11, 2022, 1:47 a.m. |
Created
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[{"model": "core.project", "pk": 1776, "fields": {"owner": null, "is_locked": false, "coped_id": "fa5fdb11-0b46-4a76-ac43-05b1981ba9be", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 7622, "created": "2022-04-11T01:32:30.470Z", "modified": "2022-04-11T01:32:30.470Z", "external_links": []}}]
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