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Article

A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2017, 7(4), 423; https://doi.org/10.3390/app7040423
Received: 8 March 2017 / Revised: 12 April 2017 / Accepted: 17 April 2017 / Published: 21 April 2017
(This article belongs to the Special Issue Distribution Power Systems)
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on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data.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. View Full-Text
Keywords: photovoltaic; power output; prediction; FOS-ELM photovoltaic; power output; prediction; FOS-ELM
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MDPI and ACS Style

Wang, J.; Ran, R.; Zhou, Y. A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm. Appl. Sci. 2017, 7, 423. https://doi.org/10.3390/app7040423

AMA Style

Wang J, Ran R, Zhou Y. A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm. Applied Sciences. 2017; 7(4):423. https://doi.org/10.3390/app7040423

Chicago/Turabian Style

Wang, Jidong, Ran Ran, and Yue Zhou. 2017. "A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm" Applied Sciences 7, no. 4: 423. https://doi.org/10.3390/app7040423

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