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Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Mohammad Monfared, Meghdad Fazeli Orcid Logo, Richard Lewis, Justin Searle Orcid Logo, Mohammad Monfared Orcid Logo

IEEE Access, Volume: 7, Pages: 91183 - 91192

Swansea University Authors: Meghdad Fazeli Orcid Logo, Justin Searle Orcid Logo, Mohammad Monfared Orcid Logo

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DOI (Published version): 10.1109/access.2019.2927804

Abstract

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa50996
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spelling 2022-05-12T12:13:38.2471829 v2 50996 2019-07-02 Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation b7aae4026707ed626d812d07018a2113 0000-0003-1448-5339 Meghdad Fazeli Meghdad Fazeli true false 0e3f2c3812f181eaed11c45554d4cdd0 0000-0003-1101-075X Justin Searle Justin Searle true false adab4560ff08c8e5181ff3f12a4c36fb 0000-0002-8987-0883 Mohammad Monfared Mohammad Monfared true false 2019-07-02 EEEG Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%. Journal Article IEEE Access 7 91183 91192 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 10 7 2019 2019-07-10 10.1109/access.2019.2927804 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University 2022-05-12T12:13:38.2471829 2019-07-02T15:56:18.6216614 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Mohammad Monfared 1 Meghdad Fazeli 0000-0003-1448-5339 2 Richard Lewis 3 Justin Searle 0000-0003-1101-075X 4 Mohammad Monfared 0000-0002-8987-0883 5 0050996-07082019130114.pdf monfared2019(2).pdf 2019-08-07T13:01:14.4330000 Output 8994881 application/pdf Version of Record true 2019-08-07T00:00:00.0000000 This work is licensed under the Creative Commons License CC BY 4.0. true eng https://creativecommons.org/licenses/by/4.0/
title Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
spellingShingle Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
Meghdad Fazeli
Justin Searle
Mohammad Monfared
title_short Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
title_full Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
title_fullStr Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
title_full_unstemmed Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
title_sort Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
author_id_str_mv b7aae4026707ed626d812d07018a2113
0e3f2c3812f181eaed11c45554d4cdd0
adab4560ff08c8e5181ff3f12a4c36fb
author_id_fullname_str_mv b7aae4026707ed626d812d07018a2113_***_Meghdad Fazeli
0e3f2c3812f181eaed11c45554d4cdd0_***_Justin Searle
adab4560ff08c8e5181ff3f12a4c36fb_***_Mohammad Monfared
author Meghdad Fazeli
Justin Searle
Mohammad Monfared
author2 Mohammad Monfared
Meghdad Fazeli
Richard Lewis
Justin Searle
Mohammad Monfared
format Journal article
container_title IEEE Access
container_volume 7
container_start_page 91183
publishDate 2019
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2019.2927804
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%.
published_date 2019-07-10T04:02:43Z
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score 11.035655

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