APPLICATION OF A HYBRID OF LEAST SQUARE SUPPORT VECTOR MACHINE AND ARTIFICIAL BEE COLONY FOR BUILDING LOAD FORECASTING

Authors

  • Mohammad Azhar Mat Daut Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia
  • Mohammad Yusri Hassan Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia
  • Hayati Abdullah Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia
  • Hasimah Abdul Rahman Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia
  • Md Pauzi Abdullah Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia
  • Faridah Hussin Centre of Electrical Energy Systems (CEES), Institute of Future Energy, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.8907

Keywords:

Load forecasting, least square support vector machine, artificial bee colony

Abstract

Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.

References

Pappas S. S., EkonomouL., KaramousantasD. C., ChatzarakisG. E., KatsikasS. K., and LiatsisP. 2008. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) Models. Energy, 33(9): 1353-1360.

Tao H., PuW., and WillisH. L. 2011. A Naive Multiple Linear Regression Benchmark For Short Term Load Forecasting. in Power and Energy Society General Meeting, 2011 IEEE. 1-6.

Dong B., CaoC., and LeeS. E. 2005. Applying Support Vector Machines To Predict Building Energy Consumption In Tropical Region," Energy and Buildings. 37(5): 545-553.

González P. A. and ZamarreñoJ. M. 2005. Prediction Of Hourly Energy Consumption In Buildings Based On A Feedback Artificial Neural Network. Energy and Buildings. 37(6): 595-601.

Foucquier A., RobertS., SuardF., StéphanL., and JayA. 2013. State Of The Art In Building Modelling And Energy Performances Prediction: A Review. Renewable and Sustainable Energy Reviews. 23(7): 272-288.

Lazos D., SproulA. B., and KayM. 2014. Optimisation Of Energy Management In Commercial Buildings With Weather Forecasting Inputs: A Review. Renewable and Sustainable Energy Reviews. 39(11): 587-603.

Samsudin P. S. R. and ShabriA. 2011. River Flow Time Series Using Least Squares Support Vector Machines. Hydrology and Earth System Sciences. 18.

Vapnik V. N. 1995. The Nature Of Statistical Learning Theory: Springer-Verlag New York, Inc.

Ji-yong S., Xiao-boZ., Xiao-weiH., Jie-wenZ., YanxiaoL., LiminH. et al. 2013. Rapid Detecting Total Acid Content And Classifying Different Types Of Vinegar Based On Near Infrared Spectroscopy And Least-Squares Support Vector Machine. Food Chemistry. 138: 192-199.

WangX., ChenJ., LiuC. and PanF. 2010. Hybrid Modeling Of Penicillin Fermentation Process Based On Least Square Support Vector Machine. Chemical Engineering Research and Design. 88: 415-420.

Gong Q., LuW., GongW., and WangX. 2014. Short-Term Load Forecasting of LSSVM Based on Improved PSO Algorithm. in Pattern Recognition. S. Li, C. Liu, and Y. Wang, Eds., ed: Springer Berlin Heidelberg, 483: 63-71.

Sheikhan M. and MohammadiN. 2012. Neural-Based Electricity Load Forecasting Using Hybrid of GA and ACO for Feature Selection. Neural Computing and Applications. 21: 1961-1970.

Shang Y. and BouffanaisR. 2014. Influence Of The Number Of Topologically Interacting Neighbors On Swarm Dynamics. Sci. Rep.4.

Karaboga D. 2005. An idea Based On Honey Bee Swarm for Numerical Optimization.

SuykensJ. A. K., GestelT. V., BrabanterJ. D., MoorB. D. and VandewalleJ. 2011.LS-SVMlab Toolbox User’s Guide.

Karaboga D. and BasturkB. 2007. A Powerful And Efficient Algorithm For Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm .Journal of Global Optimization. 39: 459-471.

Bullinaria J. and AlYahyaK. 2013. Artificial Bee Colony Training of Neural Networks," in Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). G. Terrazas, F. E. B. Otero, and A. D. Masegosa, Eds., ed: Springer International Publishing. 512: 191-201.

Karaboga D. and Ozturk C. 2011. A novel clustering approach: Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing. 11(1): 652-657.

Downloads

Published

2016-06-05

How to Cite

APPLICATION OF A HYBRID OF LEAST SQUARE SUPPORT VECTOR MACHINE AND ARTIFICIAL BEE COLONY FOR BUILDING LOAD FORECASTING. (2016). Jurnal Teknologi (Sciences & Engineering), 78(6-2). https://doi.org/10.11113/jt.v78.8907