| Feb. 13, 2024, 4:20 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 63458, "fields": {"project": 11662, "organisation": 11073, "amount": 63000, "start_date": "2017-03-01", "end_date": "2017-12-31", "raw_data": 182271}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 56296, "fields": {"project": 11662, "organisation": 11073, "amount": 63000, "start_date": "2017-03-01", "end_date": "2017-12-31", "raw_data": 160108}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 49109, "fields": {"project": 11662, "organisation": 11073, "amount": 63000, "start_date": "2017-03-01", "end_date": "2017-12-31", "raw_data": 136697}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 41862, "fields": {"project": 11662, "organisation": 11073, "amount": 63000, "start_date": "2017-03-01", "end_date": "2017-12-31", "raw_data": 107779}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
35
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{"external_links": []}
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| Nov. 21, 2023, 4:38 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 34568, "fields": {"project": 11662, "organisation": 11073, "amount": 63000, "start_date": "2017-03-01", "end_date": "2017-12-31", "raw_data": 67350}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 100832, "fields": {"project": 11662, "organisation": 16103, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 100831, "fields": {"project": 11662, "organisation": 16103, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 63497, "fields": {"project": 11662, "person": 18012, "role": "PM_PER"}}]
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| Nov. 20, 2023, 2:04 p.m. |
Updated
35
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{"title": ["", "Quality of Service for Heat Networks"], "description": ["", "\nIs it feasible to infer what a consumer requested and a district heating network delivered using high frequency \n\ndata from existing heat meters? \n\n Our feasibility study will process heat meter data from our 24 home smart network to infer what the user \n\nrequested, then compare the estimate with the actual values from 4 million datapoints it already collects and \n\nstores each day. By combining room temperature sensor data and heat meter data we will do the same for a \n\ncare home, a new heat network, and an apartment building on an existing heat network in London. We will \n\ndevelop metrics for Quality of Service - what the network delivered vs what the consumer requested - and \n\npredictive models that allow operators to run more of the network, cooler, for longer, without compromising \n\nthe user experience. \n\n Innovation: securely reading heat meters at high frequency and post-processing this this data to quantify \n\n"Quality of Service" - not just quantity of service - for heat networks in the UK and throughout Europe.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:04 p.m. |
Added
35
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{"external_links": [47512]}
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| Nov. 20, 2023, 2:04 p.m. |
Created
35
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[{"model": "core.project", "pk": 11662, "fields": {"owner": null, "is_locked": false, "coped_id": "e67058f0-63f4-4eb0-a4ba-6219534efa52", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 67333, "created": "2023-11-20T13:43:34.591Z", "modified": "2023-11-20T13:43:34.591Z", "external_links": []}}]
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