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Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation
,
Richard Lewis,
Justin Searle ,
Mohammad Monfared
IEEE Access, Volume: 7, Pages: 91183 - 91192
Swansea University Authors:
Meghdad Fazeli , Justin Searle , Mohammad Monfared
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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...
| Published in: | IEEE Access |
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| ISSN: | 2169-3536 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa50996 |
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| 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 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%. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
91183 |
| End Page: |
91192 |