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LP-SVR Model selection Using an Inexact Globalized Quasi-Newton Strategy

journal of Intelligent Learning Systems and Applications 2013年第1期5卷 19-28页

作者： Pablo Rivas-Perea juan Cota-Ruiz j. A. Perez Venzor David Garcia Chaparro j.se-gerardo rosiles Department of Computer Science Baylor University Waco TX USA Department of Electrical and Computer Engineering Auto- nomous University of Ciudad Juarez Ciudad Juárez Mexico Science Applications International Corporation El Paso TX USA.

In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems. Simulation results among standard datasets suggest that the algorithm achieves insignificant variability when measuring residual statistical properties.

关键词： Hyper-Parameter Estimation Support Vector Regression Machine Learning Data Mining

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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 j.se-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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