Photovoltaic power forecasting with a rough set combination method

Xiyun Yang, Hong Yue, Jie Ren

Research output: Contribution to conferenceProceeding

6 Citations (Scopus)
86 Downloads (Pure)

Abstract

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
Pages1-6
Number of pages6
Publication statusPublished - 1 Aug 2016
EventControl 2016 - 11th International Conference on Control - Belfast, United Kingdom
Duration: 31 Aug 20162 Sep 2016

Conference

ConferenceControl 2016 - 11th International Conference on Control
CountryUnited Kingdom
CityBelfast
Period31/08/162/09/16

    Fingerprint

Keywords

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

Cite this

Yang, X., Yue, H., & Ren, J. (2016). Photovoltaic power forecasting with a rough set combination method. 1-6. Control 2016 - 11th International Conference on Control, Belfast, United Kingdom.