Application of Cuckoo Search Algorithms to South African Short-Term Electricity Load Forecasting
Cuckoo search is one of nature-inspired algorithms successfully used for solving different optimisation problems. Cuckoo search has proved to be very effective than other nature-inspired algorithms. In this paper, a back propagation neural networks configuration is used for South African short-term electricity load forecasting. Cuckoo search algorithm was used to overcome limitations of back propagation. Nine inputs were used to train the neural networks for each of various Cuckoo search algorithms. The first set of results confirmed that Cuckoo search algorithm whose random walk step sizes were derived from Gamma probability distribution out performed other probability based cuckoo search algorithm. It obtained mean average percentage error of 5.6% and Pareto based Cuckoo search obtained 5.8%, while the original Levy based Cuckoo search algorithm performed worse with mean average percentage error of 8.4%. The second set of results confirmed that Cuckoo search with linear decreasing switching parameter outperformed other dynamic changing switching parameter cuckoo search algorithm with mean average percentage error of 6.2% followed by cuckoo search with exponentially increasing switching parameter with mean average percentage error of 6.7%.
Optimisation, Nature-Inspired Algorithms, Cuckoo Search, Probability Distribution, Neural Networks, Back Propagation
[1]
T. R. Kelley, “Optimization, an Important Stage of Engineering Design,” The Technology Teacher, vol. 69, no. 5, pp. 18-23, 2010.
[2]
E. K. Chong and S. H. Zak, An Introduction to Optimization, 2nd ed., New York: Wiley, 2001.
[3]
S. Noureddine, “An Optimization Approach for the Satisfiability Problems,” Applied Computing and Imformatics, vol. 11, no. 1, pp. 47-59, 2015.
[4]
A. R. Parkinson, R. J. Balling and J. D. Hedengren, Optimization Methods for Engineering Design: Applications and Theory, 5 ed., Brigham: Brigham Young University, 2013.
[5]
X. S. Yang, “A New Metaheuristic Bat-Inspired Algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas and N. Krasnogor, Eds., Berlin, Springer, 2010, pp. 65-74.
[6]
D. P. Rini, S. M. Shamsuddin and S. S. Yuhaniz, “Particle Swarm Optimization: Technique, System and Challanges,” International Journal of Computer Applications, vol. 14, no. 1, pp. 19-27, January 2011.
[7]
R. Storm and K. Prince, “Differential Evolution- A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, December 1997.
[8]
J. Brest, A. Zamula, I. Fister and M. S. Mauces, “Large Scale Global Optimizationusing Self-adaptive Differential Evolution Algorithm,” in WCCI 2010 IEEE World Congress on Computational Intelligence, Bacerlona, 2010.
[9]
M. Kefayat, A. L. Ara and S. N. Niaki, “A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimial placement and sizing of distributed energy resources,” Energy Conversion and Management, vol. 92, pp. 149-161, March 2015.
[10]
S. Kirkpatric, C. D. Gelatt and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 670-680, May 1983.
[11]
X.-S. Yang, Nature-Inspired Optimization algorithms, First ed., London: Elsevier, 2014.
[12]
M. I. Solihin and M. F. Zanil, “Performance Comparison of Cuckoo Search and Differential Evolution Algorithm for Constrained Optimization,” Intrnational Enginering Research and Innovation Symposium (IRIS), vol. 160, no. 1, pp. 1-7, November 2016.
[13]
M. A. Adnan and M. A. Razzaque, “A Comparative study of Particle Swarm Optimization and Cuckoo Search Techniques Through Problem - Specific Distance Function,” in 2013 International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia, 2013.
[14]
X. S. Yang and X. S. He, “Firefly Algorithm: Recent Advances and Applications,” International of Swarm Intelligence, vol. 1, no. 1, pp. 36-50, 2013.
[15]
O. Baskan, “Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Levy Flights,” Journal of Applied Mathematics, vol. 2013, pp. 1-11, 2013.
[16]
W. Buaklee and K. Hongesombut, “Optimal DG Allocation in a Smart Distribution Grid Using Cuckoo Search Algorithm,” ECTI Transactions On Electrical Engineering, Electronics And Communications, vol. 11, no. 2, pp. 16-22, August 2013.
[17]
P. Duan, K. Xie, T. Guo and X. Huang, “Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustiring Techniques,” Energies, vol. 4, pp. 173-184, 2011.
[18]
S. Mill, “Electric Load forecasting: advantages and challanges,” Electrical distribution, 13 October 2016. [Online]. Available: http://engineering.electrical-equipment.org. [Accessed 09 March 2017].
[19]
G. Singh, D. S. Chauhan, A. Chandel, D. Parashar and G. Sharma, “Factors Affecting Elements and Short Term load Forecasting Based on Multiple Linear Regression Method,” International Journal of Engineering Research and Technology, vol. 3, no. 12, pp. 736-740, December 2014.
[20]
N. Phungpornmpitak and W. Prommee, “A study of load demand forecasting models in electric power system operation and planning,” International Journal of Greater Mekong Subregion Academic and Research Network, vol. 10, pp. 19-24, 2016.
[21]
A. E. Okoye and T. C. Madueme, “A theoretical framework for enhanced forecasting of electrical loads,” International Journal of Sceintific and Research Publications, vol. 6, no. 6, pp. 554-560, June 2016.
[22]
R. Swaroop and H. A. Abdulqader, “Load Forecasting For Power System Planning And Operation Using Artificial Neural Network At AL Batinah Region OMAN,” Journal of Engineering Science and Technology, vol. 7, no. 4, pp. 498-504, 2012.
[23]
K. S. Swarup and B. Satish, “Integrated ANN Approach for Forecast Load,” IEEE Computer Applications in Power, vol. 15, no. 2, pp. 46-51, 2002.
[24]
T. Saksornchai, W. J. Lee, K. Methaprayoon, J. R. Liao and R. J. Ross, “Improve the unit scheduling by using the Neural-Network based Short Term Load Forecasting,” IEEE Transactions on Industry Applications, vol. 41, no. 1, pp. 169-179, 2005.
[25]
C. Cecati, J. Kolbusz, P. Rozycki, P. Siano and B. M. Wilamowski, “A novel RBT training algorithm for short-term electric load forecasting and comparative studies,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6519-6529, 2015.
[26]
H. S. Hippert and C. E. Pedreira, “Estimating temperature profiles for short-term load forecasting: Neural Networks compared to linear models,” IEEE Poceedings - Generation, Transmission and Distribution, vol. 151, no. 4, pp. 543-547, 2004.
[27]
W. C. Chu, Y. P. Chen, Z. W. Xu and W. J. Lee, “Multiregion short-term load forecasting in consideration of HI and load/weather diversity,” IEEE Transactions on Industry Applications, vol. 47, no. 1, pp. 232-237, 2011.
[28]
W. Charytoniuk and M. S. Chen, “Very short-term load forecasting using artificial neural networks,” IEEE Transactions on Power Systems, vol. 15, no. 1, pp. 263-268, 2000.
[29]
V. Janardhan, B. Fesmire and J. Chapman, “IT strategyin the Texas energy market,” IEEE Computer Applications in Power, vol. 15, no. 1, pp. 47-50, 2002.
[30]
A. A. da silva, M and L. S. Moulin, “Confidence intervals for neural network based short-term load forecasting,” IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1191-1196, 2000.
[31]
F. J. Marin, F. G. Lagos and F. Sandoval, “Global model for short-term load forecasting using artificial neural networks,” IEEE Proceedings - Generation, Transmission and Distribution, vol. 149, no. 2, pp. 121-125, 2002.
[32]
S. Fan, L. Chen and W. J. Lee, “Short-term load forecasting using comprehensive combination based on multimeteorological information,” IEEE Transactions on Industry Applications, vol. 45, no. 5, pp. 1460-1466, 2009.
[33]
M. De Felice and X. Yao, “Short-term load forecasting with neural network ensembles: A comparative study [Appplication Notes],” IEEE Computational Intelligence Mazagine, vol. 6, no. 3, pp. 47-56, 2011.
[34]
K. Methaprayoon, W. J. Lee, S. Rasmiddatta, J. R. Liao and R. J. Ross, “Multistage artificial neural network short-term load forecasting engine with front-end weather forecast,” IEEE Transactions on Industry Applications, vol. 43, no. 6, pp. 1410-1416, 2007.
[35]
D. Baczynski and M. Parol, “Influence of artificial neural network structure on quality of short-term electric energy consumption forecast,” IEEE Proceedings- Generation, Transmission and distribution, vol. 151, no. 2, pp. 241-245, 2004.
[36]
K. Nose-Filho, A. D. P. Lotufo and C. R. Minussi, “Short-term multinodal load forecasting using a modified general regression neural network,” IEEE Transactions on Power Delivery, vol. 26, no. 4, pp. 2862-2869, 2011.
[37]
C. Li, Y. Li, Y. Cao, J. Ma, Y. Kuang, Z. Zhang, L. Li and J. Wei, “Credibility forecasting in short-term load forecasting and its application,” IET Generation, Transmission & Distribution, vol. 9, no. 13, pp. 1564-1571, 2015.
[38]
Z. A. Bshir and M. E. El-Hawary, “Applying wavelet to short-term load forecasting using PSO-based neural networks,” IEEE Transactions on Power systems, vol. 24, no. 1, pp. 20-27, 2009.
[39]
S. H. Ling, F. H. Leung, H. K. Lam and P. K. Tam, “A novel genetic-based neural network for short-term load forecasting,” IEEE Transactions on Industrial electronics, vol. 50, no. 4, pp. 793-799, 2003.
[40]
H. Kebriael, B. N. Araabi and A. Rahimi-Kian, “Short-term load forecasting with a new nonsystemmetric penalty function,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 1817-1825, 2011.
[41]
F. M. Bianchi, E. De Santis, A. Rizzi and A. Sadeghian, “Short-term electric load forecasting using echo state networks and PCA decomposition,” IEEE Access, vol. 3, pp. 1931-1943, 2015.
[42]
N. Amiady, “Short-term bus load forecastingof power systems by a new hybrid method,” IEEE Transactions on Power systems, vol. 22, no. 1, pp. 333-341, 2007.
[43]
N. Amjady, F. Keynia and H. Zareipour, “Short-term load forecasting on microgrids by a new bilevel prediction strategy,” IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 286-294, 2010.
[44]
V. H. Ferreira and A. P. A. da Silva, “Towards estimating autonomous neural network-based electric load forecasters,” IEEE Transactions on Power systems, vol. 22, no. 4, pp. 1554-1562, 2007.
[45]
R. Zhang, Y. Dong, Y. Xu, K. Me and K. P. Wong, “Short-term load forecasting of Australian national electricity market by a ensemble model of extreme learning machine,” IET Generation, Transmission & Distribution, vol. 7, no. 4, pp. 391-397, 2013.
[46]
X.-S. Yang and S. Deb, “Engineering optimisation by Cuckoo search,” International Journal of Mathematical Modelling and Numarical Optimisation, vol. 1, no. 4, pp. 330-343, 2010.
[47]
X.-S. Yang, Nature-Inspired Optimization algorithms, First ed., London: Elsevier, 2014.
[48]
M. Sood and G. Kaur, “Speaker recongnition based on Cuckoo search algorithm,” International Journal of Innovative Technology and Exploring Engineering, vol. 2, no. 5, pp. 311-313, April 2013.
[49]
G. V. Raviteja, K. Sridevi, A. J. Rani and V. M. Rao, “Adaptive uniform circular array synthesis using Cuckoo search algorithm,” Journal of electromagnetic analysis and Applications, vol. 8, pp. 71-78, 22 April 2016.
[50]
K. A. Rani, M. A. Malek and N. Siew-Chin, “Nature-inspired Cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array,” Radio Engineering, vol. 21, no. 3, pp. 865-874, 2012.
[51]
S. Aujla and A. Ummat, “Task scheduling in cloud using hybrid Cuckoo search algorithm,” International Journal of Computer Networks and Applications, vol. 2, no. 3, pp. 144-150, May June 2015.
[52]
K. R. Babu and K. N. Sunitha, “Enhancing digital images through Cuckoo search algorithm in combination with morphological operation,” Journal of Computer Science, vol. 11, no. 1, pp. 7-17, 2015.
[53]
J. Chitra and C. S. Ravichandran, “Cuckoo search and Levy flights algorithm applied to unit-commitment problem,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 12, pp. 13670-13677, December 2014.
[54]
M. Abdel-Baset, I. M. Selim and I. M. Hezam, “Cuckoo search algorithm for stellar population analysis of galaxy,” International Journal of Information Technology and Computer Science, vol. 7, no. 11, pp. 29-33, 2015.
[55]
S. I. Tusiy, N. Shawkat, M. A. Ahmed, B. Panday and N. Sakib, “Comparative analysis on improved Cuckoo search algorithm and artificial bee colony algorithm on continouos optimization problems,” International Journal of advanced Research in Artificial Intelligence, vol. 4, no. 2, pp. 14-19, 2015.
[56]
X. Li and M. Yin, “Modified Cuckoo search algorithm with self adaptive parameter method,” Information Sciences, pp. 1-19, 8 December 2014.
[57]
M. Mareli and B. Twala, “An AdaptiveCuckoo Search Algorithm for Optimisation,” Applied Computing and Informatics, pp. 1-9, 2017.
[58]
S. Walton, O. Hassan, K. Morgan and M. R. Brown, “Modified Cuckoo search: A new gradient free optimisation,” Chaos, Solitons and Fractals, vol. 44, pp. 710-718, 22 July 2011.
[59]
P. Nasa-ngium, K. Sunat and S. Chiewchanwattana, “Enhanced modified Cuckoo search by using Mantegna Levy flights and chaotic sequences,” in 10th International Joint conference on computer Science and Software Engineering, 2013.
[60]
H. Zheng and Y. Zhou, “A novel Cuckoo search algorithm based on Gauss distribution,” Journal of Computational Information Systems, vol. 8, no. 10, pp. 4193-4200, 2012.
[61]
M. M. Zaw and E. E. Mon, “Web document clustring using Gauss distribution based Cuckoo search clustring algorithm,” International Journal of Scientific Engineering and Technology Research, vol. 3, no. 13, pp. 2945-2949, June 2014.
[62]
S. D. Ho, V. S. Vo, T. M. Le and T. T. Nguyen, “Economic emission load dispatch with multiple fuel optings using Cuckoo search algorithm with Gaussian and Cauchy distributions,” International Journal of Energy, information and Communications, vol. 5, no. 5, pp. 39-54, 2014.
[63]
T. T. Nguyen, D. N. Vo and B. H. Dinh, “Cuckoo search algorithm using different distributions for short term hydrothermal scheduling with reservoir volume constraint,” International Journal on Electrical Engineering and Informations, vol. 8, no. 1, pp. 76-92, 2016.
[64]
S. Roy, A. Mallick, S. S. Chowdhury and S. Roy, “A novel approach on Cuckoo search algorithm using Gamma distribution,” in Second International Conference on Electronics and Communication systems, 2015.
[65]
M. Mareli and B. Twala, “Global Optimisation Using Pareto Cuckoo Search Algorithm,” International Journal of Advanced Computer Research, vol. 7, no. 32, pp. 164-175, 2017.
[66]
J. H. Yi, W. H. Xu and Y. T. Chen, “Novel Back Propagation Optimisation by Cuckoo Search Algorithm,” The Scientific World Journal, vol. 2014, pp. 1-8, 2014.
