Call for Paper - November 2021 Edition
IJCA solicits original research papers for the November 2021 Edition. Last date of manuscript submission is October 20, 2021. Read More

An Effective Artificial Neural Network based Power Load Prediction Algorithm

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
Shoeb Mohammad Shahriar, Md. Khairul Hasan, Syed Refat Al Abrar
10.5120/ijca2019919050

Shoeb Mohammad Shahriar, Md. Khairul Hasan and Syed Refat Al Abrar. An Effective Artificial Neural Network based Power Load Prediction Algorithm. International Journal of Computer Applications 178(20):35-41, June 2019. BibTeX

@article{10.5120/ijca2019919050,
	author = {Shoeb Mohammad Shahriar and Md. Khairul Hasan and Syed Refat Al Abrar},
	title = {An Effective Artificial Neural Network based Power Load Prediction Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2019},
	volume = {178},
	number = {20},
	month = {Jun},
	year = {2019},
	issn = {0975-8887},
	pages = {35-41},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume178/number20/30653-2019919050},
	doi = {10.5120/ijca2019919050},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The advancement of modern technology and the speedy growth of human population has caused rapid expansion of energy consumptions. The necessity of efficient energy management and forecasting energy consumption know no bound. Developing large power system forecasting method using machine learning methods such as Artificial Neural Networks (ANN) is a prospective approach for such purpose. In recent years, load forecasting has become one of the major areas of research in Artificial Neural Network. This paper presents a model of time-series based short-term load forecasting for the dataset collected from Regional Power Control Center of a Saudi Electricity Company. Due to the potential of the architecture to take the advantages of both time series and regression methods, Artificial Neural Network performs better than other learning methods. The proposed architecture is explored by the clustering of datasets based on k-means clustering approaches and hence proved that it indeed works.

References

  1. Zhang, Mi & Xia, Changhao. (2017). A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS. International Journal of Information Technology and Computer Science. 9. 24-30. 10.5815/ijitcs.2017.04.04.
  2. Yasser Al-Rashid and Larry D. Paarmann, “Short –Term Electric Load Forecasting Using Neural Network Models”, 0-7803-3636-4/97, 1997 IEEE.
  3. P. Fishwick, ”Neural network models in simulation: A comparison with traditional modeling approaches,” Working Paper, University of Florida, Gainesville, FL,1989.
  4. https://www.worldometers.info/world-population.
  5. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  6. P. Werbos, “Generalization of backpropagation with application to recurrent gas market model”, Neural Networks, vol.1,pp.339 – 356,1988.
  7. K.Y. Lee, Y.T. Cha and J.H. Park, “Short Term Load Forecasting Using an Artificial Neural Network”, IEEE Transactions on Power Systems, Vol 1, No 1, February 1992.
  8. Mohsen Hayati and Yazdan Shirvany, “Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region”, International Journal of Electrical Computer and System Engineering Volume 1, Number 2, 2007 ISSN 1307-5179.
  9. “Load Forecasting” Chapter 12, E.A. Feinberg and Dora Genethlio, P 269–285, from links: www.ams.sunysb.edu and www.usda.gov.
  10. G.A. Adepoju, S.O.A. Ogunjuyigbe and K.O. Alawode, “Application of Neural Network to Load Forecasting in Nigerian Electrical Power System”, Volume 8, Number 1, May 2007 (Spring).
  11. Lru, K., Subbarayan, S., Shoults, R.R., Manry, M.T., Kwan, C., Lewis, F.L. and Naccarino, J., “Comparison of very short term load forecasting techniques,” IEEE Trans. Power Syst., 11(2): 877-882, May (1996).
  12. Ahmad A., Anderson T.N., Rehman S.U. (2018) Prediction of Electricity Consumption for Residential Houses in New Zealand. In: Chong P., Seet BC., Chai M., Rehman S. (eds) Smart Grid and Innovative Frontiers in Telecommunications. SmartGIFT 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 245. Springer, Cham.
  13. Senjyu, T., Mandal, P., Uezato, K. and Funabashi, T., “Next day load curve forecasting using hybrid correction method”, IEEE Trans. Power Syst., 20(1), Feb. (2005).
  14. Rahman, S. and Hazim, O., “A generalized knowledge-based short term load forecasting technique”, IEEE Trans. Power Syst., 8(2): 508-514, May (1993).
  15. Ho, K.L., Hsu, Y.Y., Liang, C.C. and Lai, T.S., “Short-term load forecasting of Taiwan power system using a knowledge-based expert system”, IEEE Trans. Power Syst.,5(4), Nov. 1990.
  16. K.-H. Kim, J.-K. Park, K.-J. Hwang, and S.-H. Kim, “Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems,” IEEE Transactions on Power Systems, vol. 10, no. 3, pp. 1534–1539, 1995.
  17. P.-F. Pai and W.-C. Hong, “Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, vol. 46, no. 17, pp. 2669–2688, 2005.
  18. T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967.
  19. Baklrtzis, A.G., Petrldis, V., Klartzis, S.J., Alexiadls, M.C. and Malssis, A.H., “A neural network short term load forecasting model for the Greek power system,” IEEE Trans. Power Syst., 11(2): 858-863, May (1996).
  20. Yu-Jun He; You-Chan Zhu; Jian-Cheng GU; Cheng-Qun Yin; Similar day selecting based neural network model and its application in short term Load forecasting, Proceedings of International Conference on Machine Learning and Cybernetics; Aug. 2005. vol. 8, p.4760-4763.
  21. M. B. Abdul Hamid and T. K. Abdul Rahman. Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm, 12th International Conference on Computer Modeling and Simulation (UKSim), 2010, p. 408-413.
  22. Hernández, Luis & Baladrón, Carlos & M Aguiar, Javier & Calavia, Lorena & Carro, Belén & Sánchez-Esguevillas, Antonio & Cook, Diane & Chinarro, David & Gómez-Sanz, Jorge. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors (Basel, Switzerland). 12. 11571-91. 10.3390/s120911571.
  23. EI Desouky, A, Aggarwal, R., Elkateb, M., Li, F., Advanced hybrid Genetic algorithm for short-term generation scheduling. IEEE Proceedings Generation, Transmission and Distribution 2001. 148 (6), p. 511-517.
  24. Liao, G.-c., Tsao, I.-P., Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting. IEEE Transactions on Evolutionary Computation 2006. (3), p.330-340.
  25. Heng, E.T.H.; Srinivasan, D.; Liew, A.C.;, Short term load forecasting using genetic algorithm and neural networks, Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on, 3-5 Mar 1998.Vol. 2, no., p. 576-581.
  26. Sudhansu Kumar Mishra, Ganapati Panda and SukadevMeher. “Multiobjective Particle Swarm Optimization Approach to Portfolio Optimization” IEEE, World Congress on Nature and Biologically Inspired Computing (NaBIC-2009), Coimbatore, India. 09-11 December 2009, pp.1612-1615.
  27. Sudhansu Kumar Mishra, Ganapati Panda and SukadevMeher, RitanjaliMajhi,“Comparative Performance Study of Multiobjective Algorithms for Financial Portfolio Design” International Journal of Computational Vision and Robotics, Inderscience publisher. Vol. 1, No.2, pp.236- 247, 2010.
  28. Tian Shu, Liu Tuanjie. Short Term Load Forecasting Based on RBFNN and QPSO,Power and Energy Conference, 27-31 March 2009.p. 1-4.
  29. Ning Lu; Jianzhong Zhou;, Particle Swarm Optimization-Based RBF Neural Network Load Forecasting Model, Power and Energy Engineering Conference, APPEEC 2009, 27-31 March 2009. Asia-Pacific, p. 1-4.
  30. Y. ShangDong and L. Xiang, "A New ANN Optimized By Improved PSO Algorithm Combined With Chaos And Its Application In Short-term Load Forecasting," 2006 International Conference on Computational Intelligence and Security, Guangzhou, 2006, pp. 945-948.

Keywords

Artificial Neural Network, Short Term Load Forecasting, Mean Average Percentage Error.