TITLE:
Forecasting the Demand of Short-Term Electric Power Load with Large-Scale LP-SVR
AUTHORS:
Pablo Rivas-Perea, Juan Cota-Ruiz, David Garcia Chaparro, Abel Quezada Carreón, Francisco J. Enríquez Aguilera, Jose-Gerardo Rosiles
KEYWORDS:
Power Load Prediction; Linear Programming Support Vector Regression; Neural Networks for Regression; Bagged Regression Trees
JOURNAL NAME:
Smart Grid and Renewable Energy,
Vol.4 No.6,
September
6,
2013
ABSTRACT: 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.