By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:3 )

Date of Conference:

14-16 Aug. 2009