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A short-term photovoltaic power prediction model based on an FOS-ELM algorithm

Wang, Jidong, Ran, Ran and Zhou, Yue 2017. A short-term photovoltaic power prediction model based on an FOS-ELM algorithm. Applied Sciences 7 (4) , 423. 10.3390/app7040423

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Abstract

With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 2076-3417
Date of First Compliant Deposit: 6 December 2018
Date of Acceptance: 17 April 2017
Last Modified: 15 Jul 2019 12:24
URI: http://orca.cf.ac.uk/id/eprint/111588

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Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

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