• DocumentCode
    1366694
  • Title

    Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity

  • Author

    Chu, Wen-Chen ; Chen, Yi Ping ; Xu, Zheng Wei ; Lee, Wei Jen

  • Author_Institution
    Tatung Univ., Taipei, Taiwan
  • Volume
    47
  • Issue
    1
  • fYear
    2011
  • Firstpage
    232
  • Lastpage
    237
  • Abstract
    The ultimate goal of an electric utility is to create maximum profit while maintaining reliability and security of the power supply. The operation and control of power system is sensitive to system demand. Therefore, improvements in load-forecasting accuracy will lead to cost savings and enhance system security. Due to Taiwan´s distinct climate characteristics, it is difficult to obtain satisfactory load-forecasting results by treating the whole island as a single region. In addition, weather factors, such as temperature, relative humidity, and the Heat Index (HI) (a human-perceived equivalent temperature) may also affect load-consumption patterns. This paper proposes a multiregion short-term load-forecasting methodology, taking into account the HI to improve load-forecasting accuracy in Taiwan Power Company´s (Taipower´s) system. The results show that adopting the HI as a parameter can effectively improve the accuracy if the temperature of the region under investigation is above 27°C (80°F). By considering both the load/weather diversity and the HI, further improvements to the load forecasting for the Taipower system during summer can be achieved.
  • Keywords
    humidity; load forecasting; meteorology; power consumption; power system control; power system security; Taiwan Power Company; Taiwan´s distinct climate characteristics; electric utility; heat index; human-perceived equivalent temperature; load consumption; load/weather diversity; multiregion short-term load forecasting; power supply reliability; power supply security; power system control; power system operation; relative humidity; Artificial neural networks; Forecasting; Heating; Humidity; Indexes; Load forecasting; Heat Index (HI); load forecasting; multiregion; neural network;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2010.2090440
  • Filename
    5617262