Forecasting power output of photovoltaic systems based on weather classification and support vector machines

Jie Shi, Wei Jen Lee, Yongqian Liu, Yongping Yang, Peng Wang

Research output: Contribution to journalArticlepeer-review

686 Scopus citations

Abstract

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

Original languageEnglish
Article number6168891
Pages (from-to)1064-1069
Number of pages6
JournalIEEE Transactions on Industry Applications
Volume48
Issue number3
DOIs
StatePublished - 2012
Externally publishedYes

Keywords

  • Forecasting
  • photovoltaic cell radiation effects
  • photovoltaic systems
  • support vector machine (SVM)
  • weather classification

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