• DocumentCode
    656811
  • Title

    Solar irradiance forecast system based on geostationary satellite

  • Author

    Zhenzhou Peng ; Shinjae Yoo ; Dantong Yu ; Dong Huang

  • Author_Institution
    Stony Brook Univ., Stony Brook, NY, USA
  • fYear
    2013
  • fDate
    21-24 Oct. 2013
  • Firstpage
    708
  • Lastpage
    713
  • Abstract
    Solar irradiance variability, left unmitigated, will threat the stability of grid system, and might incur significant economical impacts. This paper focuses on a pipeline to predict solar irradiance from 30 minutes to 5 hours using geostationary satellite. It consists of two parts: cloud motion estimation and solar irradiance prediction using the estimated satellite images. The main challenge is image noise at all levels of processing from motion estimation to irradiance prediction. To overcome this problem, we propose to use optical flow motion estimation, and subsequently combine multiple evidences together using robust support vector regression (SVR). Our systematic evaluation shows significant improvements over the baseline in both motion estimation and irradiance prediction.
  • Keywords
    image denoising; image sequences; load forecasting; motion estimation; power engineering computing; power system economics; power system stability; regression analysis; sunlight; support vector machines; SVR; cloud motion estimation; economical impact; geostationary satellite; grid stability system; image noise processing; optical flow motion estimation; pipeline; robust support vector regression; satellite image estimation; solar irradiance forecast system; solar irradiance prediction; time 30 min to 5 h; Data models; Motion estimation; Optical imaging; Pipelines; Predictive models; Robustness; Satellites;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Type

    conf

  • DOI
    10.1109/SmartGridComm.2013.6688042
  • Filename
    6688042