Conference Paper/Proceeding/Abstract 903 views
Day-Ahead Prediction of PV Generation Using Weather Forecast Data: a Case Study in the UK
,
Meghdad Fazeli ,
Richard Lewis,
Justin Searle
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
Swansea University Authors:
Mohammad Monfared , Meghdad Fazeli , Richard Lewis, Justin Searle
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DOI (Published version): 10.1109/icecce49384.2020.9179454
Abstract
With the ever-increasing of PV installations, the necessity of accurate and computationally efficient day-ahead forecasts is becoming more evident, while the solution is still a real challenge. Different techniques are being employed to transform a combination of weather forecasts and historical mea...
| Published in: | 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) |
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| ISBN: | 978-1-7281-7117-3 9781728171166 |
| Published: |
IEEE
2020
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa54786 |
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| Abstract: |
With the ever-increasing of PV installations, the necessity of accurate and computationally efficient day-ahead forecasts is becoming more evident, while the solution is still a real challenge. Different techniques are being employed to transform a combination of weather forecasts and historical measurements into PV generation predictions. In this paper, the weather forecast data, provided by the UK Met Office, and the historical measurements are used to construct three different prediction models, based on linear least square regression (LSR), artificial neural network (ANN), and fuzzy. All the models can learn from new available data while running the forecasts. The results of almost one-year study show that the best perdition (for the under-study case) is achieved by averaging the forecasts from LSR and ANN. |
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| College: |
Faculty of Science and Engineering |