Saturated Electricity Power Analysis Based on Logistic Curve Model
International Journal of Energy and Power Engineering
Volume 3, Issue 6-1, December 2014, Pages: 1-5
Received: Jul. 16, 2014;
Accepted: Jul. 22, 2014;
Published: Aug. 20, 2014
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Huiru Zhao, School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Sen Guo, School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Jia Zhou, School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Huijuan Huo, School of Economics and Management, North China Electric Power University, Beijing, 102206, China
Wanlei Xue, State Grid Shandong Electric Power Company, Power Economy & Technology Research Institute, Jinan City, Shandong Province, 250002, China
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Power load forecasting is the foundation of urban power grid planning, and saturated electricity power is a key indicator for determining the ultimate power grid scale when performing the urban power grid planning. Taken Hubei province as the empirical example, the saturated electricity power is studied by employing Logistic curve model in this paper. Firstly, the electricity power consumption and annual maximum power load of Hubei province are forecasted; then, the saturated time and scale are determined according to the judgment criteria of electricity power saturation. The calculation result shows the electricity power of Hubei province will reach saturation at 2042-2043, and the electricity power consumption and annual maximum power load will reach to 377.89 billion kWh and 66.2499 million kW, respectively.
Saturated Power Load, Logistic Curve Model, Forecasting, Hubei Province
To cite this article
Saturated Electricity Power Analysis Based on Logistic Curve Model, International Journal of Energy and Power Engineering. Special Issue: Energy Conservation and Management.
Vol. 3, No. 6-1,
2014, pp. 1-5.
Cui Kai, Li Jing-ru, Zhao Biao, et al. Research on City Saturated Load and its Forecast Methods [J]. Electric Power Technologic Economics, 2008, 20(6): 34-38.
Jiang Xin-qin, Li Xi-lan. City future saturated load forecasting based model of saturated load density [J]. Journal of Fuzhou University (Natural Science Edition), 2008, 36(4): 532-536
He Yongxiu, Wu Liangqi, Dai Aiying, et al. Combined saturation load forecast model based on system dynamics and econometrics [J]. Power Demand Side Management, 2010, 12(1): 21-25.
Wang Jing, Feng Xian-shi, Guo Hong-zhen. Urban load saturation forecast based on ant cellular automata theory [J]. Electric Power, 2011, 44(7): 17-20.
Wang Fang-dong, Lin Han, Li Chuan-dong, et al. Research on Saturated Load Macroscopically Forecast Based on Saturated Situation Analysis of Economy Curve [J]. East China Electric Power, 2010, 38(10): 1485-1490.
Wang Wei, FAang Ting-ting. The application of per⁃person electricity consumption method in saturation load forecasting [J]. Power Demand Side Management, 2012, 14(1): 21-23.
Zhang Jian-ping, Liu Jie-feng, Chen Yi-dong, et al. Saturated Load Forecasting Based on Per Capita Electricity Consumption and Per Capita Electricity Load[J]. East China Electric Power, 2014, 42(4): 661-664.
Lemeshow S, Hosmer D W. A review of goodness of fit statistics for use in the development of logistic regression models [J]. American Journal of Epidemiology, 1982, 115(1): 92-106.
Mood C. Logistic regression: Why we cannot do what we think we can do, and what we can do about it [J]. European Sociological Review, 2010, 26(1): 67-82.
Liu Jiefeng. The Research of Saturation Load Analysis Techniques and its Application [D]. Shanghai: Shanghai Jiaotong University, 2013.