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Research on Power Supply-Demand Early Warning Based on Rough Set and Support Vector Machine

Author: HuangChenFeng
Tutor: YangShuXia
School: North China Electric Power University (Beijing)
Course: Technology Economics and Management
Keywords: Power supply and demand Warning Rough Set Attribute reduction Support Vector Machine
CLC: F426.61
Type: Master's thesis
Year: 2006
Downloads: 219
Quote: 10
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Abstract


Power supply and demand early warning research must first analyze the influencing factors of electricity supply and demand , established on the basis of a set of sensitivity to reflect the warning indicator system of power supply and demand situation of different periods of historical data collection , at the same time according to the above indicators ; followed to choose the right mathematical methods , the model of early warning indicators to calculate ; combined again in different periods of China 's power supply and demand situation , determine indicators of police intelligence police limits , and divided into different police ; Finally, according to the police limit the predictive value warning degree and forecast. In this paper, different periods of historical data preprocessing , attribute reduction method based on rough sets and support vector machine prediction model to calculate the power supply and demand in 2005-2008 police intelligence indicators to predict the power supply and demand situation , the last warning degree .

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