Day-Ahead Load Forecasting using Support Vector Regression Machines

Abstract: Accurate day-ahead load prediction plays a significant role to electric companies because decisions on power system generations depend on future behavior of loads. This paper presents a strategy for short-term load forecasting that utilizes support vector regression machines. Proper data preparation, model implementation and model validation methods were introduced in this study. The SVRM model being implemented is composed of specific features, parameters, data architecture and kernel to achieve accurate pattern discovery. The developed model was implemented into an electric load forecasting system using the java open source library called LibSVM. To confirm the effectiveness of the proposed model, the performance of the developed model is evaluated through the validation set of the study and compared to other published models. The created SVRM model produced the lowest Mean Average Percentage Error (MAPE) of 1.48% and was found to be a viable forecasting technique for a day-ahead electric load forecasting system.

Lemuel Clark P. Velasco, Daisy Lou L. Polestico, Dominique Michelle M. Abella, Genesis T. Alegata and Gabrielle C. Luna. “Day-Ahead Load Forecasting using Support Vector Regression Machines”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.3 (2018). http://dx.doi.org/10.14569/IJACSA.2018.090305

@article{Velasco2018,
title = {Day-Ahead Load Forecasting using Support Vector Regression Machines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090305},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090305},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {3},
author = {Lemuel Clark P. Velasco and Daisy Lou L. Polestico and Dominique Michelle M. Abella and Genesis T. Alegata and Gabrielle C. Luna}
}