Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/60294
Title: Cross-substation short term load forecasting using support vector machine
Authors: Jonglak Pahasa
Nipon Theera-Umpon
Keywords: Computer Science
Engineering
Issue Date: 6-Oct-2008
Abstract: This paper investigates the behavior of a short term load forecasting system in the cross-substation scheme. The proposed forecasting system is based on the support vector machine with the input features of past loads and temperature. It is trained with the data from one substation and tested on the blind-test data from other substations. A set of real-world data from 4 substations in Bangkok, i.e., Bangkok Noi, North Bangkok, South Thonburl and Rangsit, is used in the experiments. The results show that the similarities of the daily load's amplitude ranges and patterns of the training substations and the test substations is required to perform the cross-substation forecasting. This observation is beneficial to the model development in that the retraining stage at a new substation may be omitted if the similarities are obeyed. © 2008 IEEE.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=52949095023&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/60294
Appears in Collections:CMUL: Journal Articles

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