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http://bura.brunel.ac.uk/handle/2438/31448Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hulak, D | - |
| dc.contributor.author | Xie, Y | - |
| dc.contributor.author | Taylor, G | - |
| dc.coverage.spatial | Cardiff, UK | - |
| dc.date.accessioned | 2025-06-11T14:27:16Z | - |
| dc.date.available | 2025-06-11T14:27:16Z | - |
| dc.date.issued | 2024-09-02 | - |
| dc.identifier | ORCiD: Daniil Hulak https://orcid.org/0000-0001-8840-3557 | - |
| dc.identifier | ORCiD: Gareth Taylor https://orcid.org/0000-0003-0867-2365 | - |
| dc.identifier.citation | Hulak, D., Xie, Y. and Taylor, G. (2024) 'Performance Analysis of Photovoltaic Installations Based on Machine Learning Techniques', 2024 59th International Universities Power Engineering Conference, UPEC 2024, Cardiff, UK, 2-6 September, pp. 1 - 6. doi: 10.1109/UPEC61344.2024.10892390. | en_US |
| dc.identifier.isbn | 979-8-3503-7973-0 (ebk) | - |
| dc.identifier.isbn | 979-8-3503-7974-7 (PoD) | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/31448 | - |
| dc.description.abstract | This paper investigates the novel performance analysis of photovoltaic (PV) installations by applying machine learning techniques (ML). The data used for the research is a mixed dataset of data from an experimental PV installation located at Brunel University London with correspondingly available weather data. Firstly, the analysis aims to establish various sensitivity relationships between PV power generation and weather conditions using techniques based on Random Forest ML. Secondly, the processing stage is implemented to assess the fitness of the different ML techniques through cross-validation. The results highlight the differing effectiveness of the applied approaches in achieving accurate and reliable results for the PV installations. The best techniques offer valuable insights for optimizing renewable energy usage in diverse environmental conditions. | en_US |
| dc.format.extent | 1 - 6 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language | English | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
| dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
| dc.source | 59th International Universities Power Engineering Conference (UPEC) | - |
| dc.source | 59th International Universities Power Engineering Conference (UPEC) | - |
| dc.subject | generation forecasting | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | photovoltaic installation | en_US |
| dc.subject | solar irradiance | en_US |
| dc.title | Performance Analysis of Photovoltaic Installations Based on Machine Learning Techniques | en_US |
| dc.type | Conference Paper | en_US |
| dc.date.dateAccepted | 2024-06-15 | - |
| dc.identifier.doi | https://doi.org/10.1109/UPEC61344.2024.10892390 | - |
| dc.relation.isPartOf | 2024 59th International Universities Power Engineering Conference, UPEC 2024 | - |
| pubs.finish-date | 2024-09-06 | - |
| pubs.finish-date | 2024-09-06 | - |
| pubs.publication-status | Published | - |
| pubs.start-date | 2024-09-02 | - |
| pubs.start-date | 2024-09-02 | - |
| dcterms.dateAccepted | 2024-06-15 | - |
| dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | 1.01 MB | Adobe PDF | View/Open |
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