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The modeling of the relationships between the power loads and the variables that influence the power loads especially in the abnormal days is the key point to improve the performance of short-term load forecasting systems. To integrate the advantages of several forecasting models for improving the forecasting accuracy, based on data mining and artificial neural network techniques, an ensemble decision tree and FLANN combining short-term load forecasting system is proposed to mainly settle the weather-sensitive factors' influence on the power load.
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