Chen, Bo-JuenBo-JuenChenChang, Ming-WeiMing-WeiChangLin, Chih-JenChih-JenLin2009-05-062018-07-052009-05-062018-07-05200408858950http://ntur.lib.ntu.edu.tw//handle/246246/155247https://www.scopus.com/inward/record.uri?eid=2-s2.0-9244240793&doi=10.1109%2fTPWRS.2004.835679&partnerID=40&md5=641946b5c2a3864f2ed5a2a4ad0bf382Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting. © 2004 IEEE.application/pdf431959 bytesapplication/pdfen-USLoad forecasting; Regression; Support vector machines; Time seriesLearning systems; Mathematical models; Regression analysis; Time series analysis; Daily load demand; Mid term load forecasting; Support vector machines; Electric load forecastingjournal article2-s2.0-9244240793http://ntur.lib.ntu.edu.tw/bitstream/246246/155247/1/25.pdf