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Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation / Mohammad Monfared; Meghdad Fazeli; Richard Lewis; Justin Searle

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

Swansea University Author: Fazeli, Meghdad

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: 2019
Online Access: Check full text

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%.
College: College of Engineering
Start Page: 91183
End Page: 91192