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《Automation of Electric Power Systems》 2004-03
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APPLIED DATA MINING MODELS FOR SHORT-TERM LOAD FORECASTING

Zhu Liuzhang, Yuan Lin, Huang Taigui (Dispatching & Communication Center of Anhui Electric Power Corp, Hefei 230061, China)  
Based on decision tree algorithm of data mining and general decision support objects (DSO), an efficient model-building tool, this paper designed and implemented the decision tree formed data mining models and used them to the daily load forecasting in terms of the weather-load database of district power network. It first described decision tree classification theory, analyzed the constructing process of decision-tree data mining models for daily load forecasting and then presented their realization by programming with DSO. It further offered the whole process of load forecasting with the decision tree algorithm and its actual forecasting results. The statistic analysis showed that the data mining models totally reached the applied standard. It is intelligently adaptive, self-learning and automatic, reliable and highly accurate.
【CateGory Index】: TM769
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