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Non intrusive load monitoring & identification for energy management system using computational intelligence approach
Includes bibliography.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2015
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| _version_ | 1867613203282788352 |
|---|---|
| access_status_str | Open Access |
| author | Aladesanmi, Ereola Johnson |
| author2 | Folly, Komla A |
| author_browse | Aladesanmi, Ereola Johnson Folly, Komla A |
| author_facet | Folly, Komla A Aladesanmi, Ereola Johnson |
| author_sort | Aladesanmi, Ereola Johnson |
| collection | Thesis |
| description | Includes bibliography. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/13561 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:24.523Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/13561 Non intrusive load monitoring & identification for energy management system using computational intelligence approach Aladesanmi, Ereola Johnson Folly, Komla A Awodele, Kehinde Electrical Engineering Includes bibliography. Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval. 2015-07-29T03:42:03Z 2015-07-29T03:42:03Z 2015 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/13561 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Aladesanmi, Ereola Johnson Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| thesis_degree_str | Master's |
| title | Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| title_full | Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| title_fullStr | Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| title_full_unstemmed | Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| title_short | Non intrusive load monitoring & identification for energy management system using computational intelligence approach |
| title_sort | non intrusive load monitoring identification for energy management system using computational intelligence approach |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/13561 |
| work_keys_str_mv | AT aladesanmiereolajohnson nonintrusiveloadmonitoringidentificationforenergymanagementsystemusingcomputationalintelligenceapproach |