Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting

Entropy

Miloš Božić²  Miloš Stojanović¹  Zoran Stajić² 

[1] School of Higher Technical Professional Education, Aleksandra Medvedevа 20, 18000 Niš, Serbia; E-Mail:;Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia; E-Mail:

关键词: input selection;    mutual information;    electric load forecasting;    least squares support vector machines;   

DOI  :  10.3390/e15030926

来源: mdpi

【 摘 要 】

Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI) has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs) load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.