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Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations

International Journal of Intelligence Science 2013年第1期3卷 5-14页

作者： Pablo Rivas-Perea Juan Cota-Ruiz David Garcia Chaparro Jorge Arturo Perez Venzor abel quezada carreón Jose Gerardo Rosiles Department of Computer Science School of Engineering & Computer Science Baylor University Waco USA Department of Electrical & Computer Engineering Autonomous University of Ciudad Juárez Ciudad Juárez México Rosiles Consulting El Paso USA

Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We review the general concept of support vector machines (SVMs), address the state-of-the-art on training methods SVMs, and explain the fundamental principle of SVRs. The most common learning methods for SVRs are introduced and linear programming-based SVR formulations are explained emphasizing its suitability for large-scale learning. Finally, this paper also discusses some open problems and current trends.

关键词： Support Vector Machines Support Vector Regression Linear Programming Support Vector Regression

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Forecasting the Demand of Short-Term Electric Power Load with Large-Scale LP-SVR

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Smart Grid and Renewable Energy 2013年第6期4卷 449-457页

作者： Pablo Rivas-Perea Juan Cota-Ruiz David Garcia Chaparro abel quezada carreón Francisco J. Enríquez Aguilera Jose-Gerardo Rosiles Department of Computer Science School of Engineering and Computer Science Baylor University Waco USA Department of Electrical and Computer Engineering Autonomous University of Ciudad Juárez (UACJ) Ciudad Juárez México Science Applica- tions International Corporation El Paso USA.

This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward neural networks (FFnn), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the new England Power Pool (nEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFnn approach has a 1.61%. Similarly, the FFnn method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFnn was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFnn and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.

关键词： Power Load Prediction Linear Programming Support Vector Regression neural networks for Regression Bagged Regression Trees

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