FORECAST TECHNIQUE OF ELECTRIC NETWORK LOADS IN CENTRAL CHINA BASED ON METEOROLOGICAL ELEMENTS

Hu Jianglin; Chen Zhenghong; Hong Bin; Wang Guangsheng

Vol.13, NO.5, 2002

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2002 > 13(5): 600-608

Hu Jianglin, Chen Zhenghong, Hong Bin, et al. Forecast technique of electric network loads in central China based on meteorological elements. J Appl Meteor Sci, 2002, 13(5): 600-608.

Citation:

Hu Jianglin, Chen Zhenghong, Hong Bin, et al. Forecast technique of electric network loads in central China based on meteorological elements. J Appl Meteor Sci, 2002, 13(5): 600-608.

Hu Jianglin, Chen Zhenghong, Hong Bin, et al. Forecast technique of electric network loads in central China based on meteorological elements. J Appl Meteor Sci, 2002, 13(5): 600-608.

Citation:

Hu Jianglin, Chen Zhenghong, Hong Bin, et al. Forecast technique of electric network loads in central China based on meteorological elements. J Appl Meteor Sci, 2002, 13(5): 600-608.

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FORECAST TECHNIQUE OF ELECTRIC NETWORK LOADS IN CENTRAL CHINA BASED ON METEOROLOGICAL ELEMENTS

1.
Meteorological Research Institute of Hubei Province, Wuhan 430074
2.
Energy Research Institute of Electric Power Bureau of Centre China, Wuhan 430074

Received Date: 2001-04-29
Rev Recd Date: 2001-12-11
Publish Date: 2002-10-31

Abstract

Abstract

Based on the variation analysis of electric network loads on various holidays, the models for forecasting the daily load of the electric power network and its maximum and minimum loads per day in the Central China are given.Using meteorological variables as predictor, the dynamical colligating linear regression combined with auto-regression and the dynamical nonlinear artificial neural network methods are used.The prediction with 365 independent samplesshows that the methods have a high precision in electric load forecasting in Central China and can be used for the operational practice.
- Elect ric loads;
- Linear reg ression;
- Art if icial neural network;
- Meteorological elements

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References(6)

Figures and Tables

图 1 1997年2月1日至2000年4月30日华中电网逐日的气象负荷 (l_m, 单位:%) 和气温变量 (T_n, 单位:℃) 的时间序列变化关系

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图 2 365天的独立样本试验结果 (a) 动态混合线性回归方法法; (b) 人工神经网络方法 (图中上、中、下曲线分别为日最大、平均和最小电量负荷 (单位:10⁶kW) 的预测 (虚线) 和实况 (实线))

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表 1 华中电网日负荷、日最大及日最小负荷在春节期间的节假日效应项l_h

表 2 试验期间华中电网负荷的预测效果

表 3 试验期间华中电网日负荷、日最大及日最小负荷预报误差分布

表 4 华中四省各省电网日负荷、日最大及日最小负荷的预测平均误差

References

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