Photovoltaic power forecasting with a rough set combination method

Xiyun Yang, Hong Yue, Jie Ren

Research output: Contribution to conferenceProceedingpeer-review

12 Citations (Scopus)
103 Downloads (Pure)


One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.
Original languageEnglish
Number of pages6
Publication statusPublished - 1 Aug 2016
EventControl 2016 - 11th International Conference on Control - Belfast, United Kingdom
Duration: 31 Aug 20162 Sept 2016


ConferenceControl 2016 - 11th International Conference on Control
Country/TerritoryUnited Kingdom


  • photovoltaic (PV) power
  • combination model forecasting
  • rough set
  • individual model forecasting


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