Lai, CS orcid.org/0000-0002-4169-4438, Li, X, Lai, LL et al. (1 more author) (2017) Daily clearness index profiles and weather conditions studies for photovoltaic systems. In: Energy Procedia. 9th International Conference on Applied Energy, ICAE2017, 21-24 Aug 2017, Cardiff, UK. Elsevier , pp. 77-82.
Abstract
The increasing number of distributed photovoltaic (PV) systems connected to the power grid has made system planning and performance evaluation a challenging task. This is mainly due to the computational complexity, such as load flow analysis with large irradiance datasets collected from various locations of the installed PV farms. Solar irradiance data are known to possess the characteristic of high uncertainty, due to the random nature of cloud cover and atmospheric conditions. This paper presents the studies on the relationships of clustered clearness index profiles and the weather conditions obtained from the weather forecasting stations. Four years of solar irradiance and weather conditions data from two locations (Johannesburg and Kenya) were obtained and are used for the analysis. The preliminary study shows that the weather condition is related to the daily clearness index profiles. This work will form the basis for estimating the daily clearness index profile with weather conditions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. Published under creative commons licence CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | photovoltaic system; clustering; clearness index |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jul 2018 11:49 |
Last Modified: | 30 Jul 2018 11:50 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.egypro.2017.12.013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133923 |