PDF(3225 KB)
Study on urban short-term gas load forecasting based on support vector machine model
Chao ZHANG, Yi LIU, Hui ZHANG, Hong HUANG
Journal of Tsinghua University(Science and Technology) ›› 2014, Vol. 54 ›› Issue (3) : 320-325.
PDF(3225 KB)
PDF(3225 KB)
Study on urban short-term gas load forecasting based on support vector machine model
Natural gas is green and efficient energy which is widely used in industrial production and daily life. Daily gas load forecasting is helpful for scientifically and rationally supplying. Therefore, the forecasting results are beneficial to practical work. A forecasting model for daily gas loads was developed based on support vector machine theory. The gas load data of a North-China city were taken as a sample to verify the forecasting accuracy, with the main factors that affect the daily gas load as well as their effects on model accuracies being discussed. Several data normalization methods were used with the forecasting accuracy based on normalization methods analyzed. The developed model performs well with the error less than 5% for the through-year data, and less than 2% for the heating period data. The discussion about forecasting accuracies in this paper may be helpful for similar problems.
gas load forecasting / support vector machine / data normalization method
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