History of changes to: "Smart meters, data mining and machine learning: identifying energy use profiles and providing feedback that empowers energy demand reduction"
Date Action Change(s) User
Nov. 27, 2023, 2:13 p.m. Added 35 {"external_links": []}
Nov. 20, 2023, 2:03 p.m. Added 35 {"external_links": []}
Nov. 13, 2023, 1:34 p.m. Added 35 {"external_links": []}
Nov. 6, 2023, 1:31 p.m. Added 35 {"external_links": []}
Aug. 14, 2023, 1:31 p.m. Added 35 {"external_links": []}
Aug. 7, 2023, 1:32 p.m. Added 35 {"external_links": []}
July 31, 2023, 1:34 p.m. Added 35 {"external_links": []}
July 24, 2023, 1:36 p.m. Added 35 {"external_links": []}
July 17, 2023, 1:35 p.m. Added 35 {"external_links": []}
July 10, 2023, 1:26 p.m. Added 35 {"external_links": []}
July 3, 2023, 1:27 p.m. Added 35 {"external_links": []}
June 26, 2023, 1:26 p.m. Added 35 {"external_links": []}
June 19, 2023, 1:27 p.m. Added 35 {"external_links": []}
June 12, 2023, 1:29 p.m. Added 35 {"external_links": []}
June 5, 2023, 1:34 p.m. Added 35 {"external_links": []}
May 29, 2023, 1:28 p.m. Added 35 {"external_links": []}
May 22, 2023, 1:29 p.m. Added 35 {"external_links": []}
May 15, 2023, 1:32 p.m. Added 35 {"external_links": []}
May 8, 2023, 1:37 p.m. Added 35 {"external_links": []}
May 1, 2023, 1:28 p.m. Added 35 {"external_links": []}
April 24, 2023, 1:35 p.m. Added 35 {"external_links": []}
April 17, 2023, 1:28 p.m. Added 35 {"external_links": []}
April 10, 2023, 1:25 p.m. Added 35 {"external_links": []}
April 3, 2023, 1:26 p.m. Added 35 {"external_links": []}
Jan. 28, 2023, 11:09 a.m. Created 43 [{"model": "core.projectfund", "pk": 29819, "fields": {"project": 7038, "organisation": 2, "amount": 0, "start_date": "2019-09-30", "end_date": "2022-12-31", "raw_data": 49383}}]
Jan. 28, 2023, 10:52 a.m. Added 35 {"external_links": []}
April 11, 2022, 3:48 a.m. Created 43 [{"model": "core.projectfund", "pk": 21953, "fields": {"project": 7038, "organisation": 2, "amount": 0, "start_date": "2019-09-30", "end_date": "2022-12-31", "raw_data": 32521}}]
April 11, 2022, 3:48 a.m. Created 41 [{"model": "core.projectorganisation", "pk": 82825, "fields": {"project": 7038, "organisation": 44, "role": "LEAD_ORG"}}]
April 11, 2022, 3:48 a.m. Created 40 [{"model": "core.projectperson", "pk": 51361, "fields": {"project": 7038, "person": 9844, "role": "STUDENT_PER"}}]
April 11, 2022, 1:48 a.m. Updated 35 {"title": ["", ""Smart meters, data mining and machine learning: identifying energy use profiles and providing feedback that empowers energy demand reduction""], "description": ["", "\n"My doctoral research would involve interviews with key stakeholders in the UK smart meter rollout at a critical point in the initiative - the 2020 government deadline - while also using novel methods of data mining and machine learning to generate meaningful energy consumption profiles from large, emerging datasets. The insight from these two strands of research will then be applied to a trial energy feedback intervention. This research will be a valuable contribution to energy social science by both capturing this moment in the UK's ambitious smart meter rollout and informing future public engagement for energy demand reduction through smart grids. Research Questions: This doctoral research would be undertaken through thesis by publication, with each publication exploring one of these three interrelated questions:\n1. How do key stakeholders - energy suppliers, energy consumers, and various intermediaries - perceive the smart grid in terms of data access, value and intelligibility?\n2. Can data mining and machine learning translate large datasets of smart meter data into\nenergy consumption profiles that identify inefficiency or other patterns like time shifting\nand flexibility?\n3. What feedback and interventions based on half-hourly energy consumption profiles lead to energy demand reduction?\nTheoretical Framework / Methodology: A starting point for the theoretical framework and methodology of this doctoral research is the socio-technical transitions perspective developed by energy researchers (Geels et al 2018, McKenna et al 2018, Eyre et al 2018). I also have been influenced by a critique of the academic rigour of energy social science (Sovacool et al 2018). As I come from a communications and education background, my research will also be informed by diffusion theory, the elaboration likelihood model, and the theory of planned behaviour (Rogers 2010, Petty and Cacioppo 1986, Ajzen 2005). While behavioural economics provides a theoretical framework that greatly influences public policy in the UK, Hampton and Adams (2018) argue for the incorporation of practice theory into the energy policy discourse. Practice theory has also been used to explore different conceptualisations of energy users in smart grids - energy consumers vs. energy citizens (Goulden et al 2014). Fell and Shipworth (2017) propose a theory-agnostic approach to get past this divide, using a Dimension-Set Framework which characterises demand-side responses by electricity relevant dimensions and a phase state of possible options. By using the tools of data science to identify energy consumption profiles and patterns of potential efficiencies, I'm applying practice theory in the realm of big data."\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Active"]}
April 11, 2022, 1:48 a.m. Added 35 {"external_links": [25572]}
April 11, 2022, 1:48 a.m. Created 35 [{"model": "core.project", "pk": 7038, "fields": {"owner": null, "is_locked": false, "coped_id": "ef99166b-e123-48b0-9ba8-07fbe2654362", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 32505, "created": "2022-04-11T01:44:34.192Z", "modified": "2022-04-11T01:44:34.192Z", "external_links": []}}]