An Improved Building Load Forecasting Method using a combined Least Square Support Vector Machine and modified Artificial Bee Colony

Authors

  • Mohammad Azhar Mat Daut Faculty of Electrical Engineering, Universiti Teknologi Malaysia
  • Mohammad Yusri Hassan Faculty of Electrical Engineering, Universiti Teknologi Malaysia
  • Hayati Abdullah Faculty of Mechanical Engineering, Universiti Teknologi Malaysia
  • Hasimah Abdul Rahman
  • Md Pauzi Abdullah Faculty of Electrical Engineering, Universiti Teknologi Malaysia
  • Faridah Hussin Faculty of Electrical Engineering, Universiti Teknologi Malaysia

DOI:

https://doi.org/10.11113/elektrika.v16n1.22

Abstract

This paper presents an improved building load forecasting method using a combined Least Square Support Vector Machine and modified Artificial Bee Colony. The main contribution of the proposed method is the improvement in the exploitation capability of the standard Artificial Bee Colony, in which a different probability selection has been introduced. This was achieved by changing the standard probability selection with the clonal selection algorithm. The results from two other methods were compared with the results from the proposed method to validate the performance of the proposed forecasting method. The accuracy of the proposed method was evaluated using the Mean Absolute Error, Mean Absolute Percentage Error and Root Mean Square Error. It was found that the proposed method had improved the accuracy by more than 50 % compared to the other methods. The results of the study showed that the proposed method has great potential to be used as an accurate forecasting method.

Author Biography

Hasimah Abdul Rahman

Faculty of Electrical Engineering, Universiti Teknologi Malaysia

References

S. M. Islam, S. M. Al-Alawi, and K. A. Ellithy, "Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network," Electric Power Systems Research, vol. 34, pp. 1-9, 7// 1995.

I. S. Markham and T. R. Rakes, "The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression," Computers & Operations Research, vol. 25, pp. 251-263, 4// 1998.

B. Dong, C. Cao, and S. E. Lee, "Applying support vector machines to predict building energy consumption in tropical region," Energy and Buildings, vol. 37, pp. 545-553, 5// 2005.

A. Foucquier, S. Robert, F. Suard, L. Stéphan, and A. Jay, "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, vol. 23, pp. 272-288, 7// 2013.

C.-M. Vong, P.-K. Wong, and Y.-P. Li, "Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference," Engineering Applications of Artificial Intelligence, vol. 19, pp. 277-287, 4// 2006.

X. Youshen, H. Leung, and H. Chan, "A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines," Circuits and Systems II: Express Briefs, IEEE Transactions on, vol. 53, pp. 62-66, 2006.

D. Karaboga, "An idea based on Honey Bee Swarm for Numerical Optimization," 2005.

Z. Mustaffa, Y. Yusof, and S. S. Kamaruddin, "Application of LSSVM by ABC in energy commodity price forecasting," in Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, 2014, pp. 94-98.

Osman Hegazy, Omar S. Soliman, and M. A. Salam, "LSSVM-ABC Algorithm for Stock Price prediction," International Journal of Computer Trends and Technology (IJCTT)2014., p. 12, 2014.

W.-C. Hong, "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, vol. 36, pp. 5568-5578, 9// 2011.

B. Babayigit and R. Ozdemir, "A modified artificial bee colony algorithm for numerical function optimization," in Computers and Communications (ISCC), 2012 IEEE Symposium on, 2012, pp. 000245-000249.

V. N. Vapnik, The nature of statistical learning theory: Springer-Verlag New York, Inc., 1995.

C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, pp. 273-297, 1995/09/01 1995.

H. Fazeli, R. Soleimani, M.-A. Ahmadi, R. Badrnezhad, and A. H. Mohammadi, "Experimental Study and Modeling of Ultrafiltration of Refinery Effluents Using a Hybrid Intelligent Approach," Energy & Fuels, vol. 27, pp. 3523-3537, 2013/06/20 2013.

M. A. Ahmadi, M. Ebadi, and S. M. Hosseini, "Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach," Fuel, vol. 117, Part A, pp. 579-589, 1/30/ 2014.

J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, LS-SVMlab Toolbox User’s Guide, 2011.

L. N. de Castro and F. J. Von Zuben, "Learning and optimization using the clonal selection principle," Evolutionary Computation, IEEE Transactions on, vol. 6, pp. 239-251, 2002.

Downloads

Published

2017-04-20

How to Cite

Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Abdul Rahman, H., Abdullah, M. P., & Hussin, F. (2017). An Improved Building Load Forecasting Method using a combined Least Square Support Vector Machine and modified Artificial Bee Colony. ELEKTRIKA- Journal of Electrical Engineering, 16(1), 1–5. https://doi.org/10.11113/elektrika.v16n1.22

Issue

Section

Articles