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M. M. Dalvand, S. B. Z. Azami, H. Tarimoradi and Ieee, “Long-Term Load Forecasting of Iranian Power Grid Using Fuzzy and Artificial Neural Networks,” 43rd International-Universities-Power-Engineering Conference, Padova, 2008, pp. 559-562.

has been cited by the following article:

• TITLE: Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

  AUTHORS: Shijie Ye, Guangfu Zhu, Zhi Xiao

  KEYWORDS: Long Term Load Forecasting; Support Vector Regression; China

  JOURNAL NAME: Energy and Power Engineering, Vol.4 No.5, September 26, 2012

  ABSTRACT: Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China’s 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.