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Knowledge-Enabled Short-Term Load Forecasting Based on Pattern-Base Using Classification & Regression Tree and Support Vector Regression

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1 Author(s)
Ying-Chun Guo ; Coll. of Math. & Comput. Sci., Hebei Univ., Baoding, China

The paper presents a new model of Short-term load forecasting based on pattern-base. It can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree; secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar features; thirdly, it establishes support vector regression forecasting model based on the pattern-base which matches to the forecasting day. The model has many advantages: first, since the training data has similar pattern to the forecasting day, the model reflects the rule of daily load accurately and improves forecasting precision accordingly; second, as the pattern variables need not to be input into model, the mapping of the categorical variables is solved; third, as inputs are reduced, the model is simplified and the runtime is lessened.

Published in:

2009 Fifth International Conference on Natural Computation  (Volume:3 )

Date of Conference:

14-16 Aug. 2009