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Improving Energy Demand Estimation Using an Adaptive Firefly Algorithm

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 873))

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Abstract

In recent years, energy demand has increased rapidly in developing countries. Energy demand estimation (EDE) plays an important role for policy makers and related organizations. Generally, energy demand can be mathematically modelled by population, economic, and other indicators using various forms of equations. However, it is difficult to choose optimal or near-optimal weighting factors for these models. In this paper, an adaptive firefly algorithm (AFA) is proposed to improve the efficiency of energy demand estimation in Turkey. Two different estimation models, including linear and quadratic forms, are used. Historical data in Turkey from 1979 to 2005 is utilized for training and testing these models. Experimental results show that our approach achieves better relative estimation errors than two other existing algorithms, ant colony (ACO) and particle swarm optimization (PSO).

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References

  1. BP Group: BP statistical review of world energy June 2017. Technical report. BP Company (2017)

    Google Scholar 

  2. Energy Information Association: Annual energy outlook 2017 with projections to 2050. US Department of Energy (2017)

    Google Scholar 

  3. Toksarı, M.D.: Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35(8), 3984–3990 (2007)

    Article  Google Scholar 

  4. Ünler, A.: Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025. Energy Policy 36(6), 1937–1944 (2008)

    Article  Google Scholar 

  5. Yu, S., Wei, Y.M., Wang, K.: A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy 42, 329–340 (2012)

    Article  Google Scholar 

  6. Canyurt, O.E., Ozturk, H.K.: Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey. Energy Policy 36(7), 2562–2569 (2008)

    Article  Google Scholar 

  7. Askarzadeh, A.: Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: a case study of Iran. Energy 72, 484–491 (2014)

    Article  Google Scholar 

  8. Wu, Q., Peng, C.: A hybrid BAG-SA optimal approach to estimate energy demand of China. Energy 120, 985–995 (2017)

    Article  Google Scholar 

  9. Sonmez, M., Akgüngör, A.P., Bektaş, S.: Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy 122, 301–310 (2017)

    Article  Google Scholar 

  10. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)

    Google Scholar 

  11. Torres-Treviño, L.M.: Let the swarm be: an implicit elitism in swarm intelligence. Int. J. Bio-Inspired Comput. 9(2), 65–76 (2017)

    Article  Google Scholar 

  12. Zhao, J., Lv, L., Wang, H., Sun, H., Wu, R., Nie, J., Xie, Z.: Particle swarm optimization based on vector Gaussian learning. KSII Trans. Internet Inf. Syst. 11(4), 2038–2057 (2017)

    Google Scholar 

  13. Wang, H., Sun, H., Li, C.H., Rahnamayan, S., Pan, J.S.: Diversity enhanced particle swarm optimization with neighborhood search. Inf. Sci. 223, 119–135 (2013)

    Article  MathSciNet  Google Scholar 

  14. Sun, H., Wang, K., Zhao, J., Yu, X.: Artificial bee colony algorithm with improved special centre. Int. J. Comput. Sci. Math. 7(6), 548–553 (2016)

    Article  MathSciNet  Google Scholar 

  15. Yu, G.: A new multi-population-based artificial bee colony for numerical optimization. Int. J. Comput. Sci. Math. 7(6), 509–515 (2016)

    Article  MathSciNet  Google Scholar 

  16. Wang, H., Wu, Z.J., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)

    Article  MathSciNet  Google Scholar 

  17. Lv, L., Wu, L.Y., Zhao, J., Wang, H., Wu, R.X., Fan, T.H., Hu, M., Xie, Z.F.: Improved multi-strategy artificial bee colony algorithm. Int. J. Comput. Sci. Math. 7(5), 467–475 (2016)

    Article  MathSciNet  Google Scholar 

  18. Cui, L.Z., Li, G.H., Lin, Q.Z., Du, Z.H., Gao, W.F., Chen, J.Y., Lu, N.: A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf. Sci. 367–368, 1012–1044 (2016)

    Article  Google Scholar 

  19. Yu, G.: An improved firefly algorithm based on probabilistic attraction. Int. J. Comput. Sci. Math. 7(6), 530–536 (2016)

    Article  MathSciNet  Google Scholar 

  20. Wang, H., Cui, Z., Sun, H., Rahnamayan, S., Yang, X.S.: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft. Comput. 21(18), 5325–5339 (2017)

    Article  Google Scholar 

  21. Lv, L., Zhao, J.: The firefly algorithm with Gaussian disturbance and local search. J. Signal Process. Syst. 90, 1123–1131 (2017). https://doi.org/10.1007/s11265-017-1278-y. (in press)

    Article  Google Scholar 

  22. Cui, Z., Sun, B., Wang, G., Xue, Y., Chen, J.: A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J. Parallel Distrib. Comput. 103, 42–52 (2017)

    Article  Google Scholar 

  23. Zhang, M., Wang, H., Cui, Z., Chen, J.: Hybrid multi-objective cuckoo search with dynamical local search. Memet. Comput. 10, 199–208 (2017). https://doi.org/10.1007/s12293-017-0237-2. (in press)

    Article  Google Scholar 

  24. Cai, X., Gao, X.Z., Xue, Y.: Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int. J. Bio-Inspired Comput. 8(4), 205–214 (2016)

    Article  Google Scholar 

  25. Marichelvam, M.K., Geetha, M.: A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time. Int. J. Bio-Inspired Comput. 8(5), 318–325 (2016)

    Article  Google Scholar 

  26. Wang, H., Wang, W., Sun, H., Rahnamayan, S.: Firefly algorithm with random attraction. Int. J. Bio-Inspired Comput. 8(1), 33–41 (2016)

    Article  Google Scholar 

  27. Kaur, M., Sharma, P.K.: On solving partition driven standard cell placement problem using firefly-based metaheuristic approach. Int. J. Bio-Inspired Comput. 9(2), 121–127 (2017)

    Article  Google Scholar 

  28. Wang, H., Wang, W.J., Zhou, X.Y., Sun, H., Zhao, J., Yu, X., Cui, Z.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382–383, 374–387 (2017)

    Article  Google Scholar 

  29. dos Santos Coelho, L., Mariani, V.C.: Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build. 59, 273–278 (2013)

    Article  Google Scholar 

  30. Wang, H., Zhou, X.Y., Sun, H., Yu, X., Zhao, J., Zhang, H., Cui, L.Z.: Firefly algorithm with adaptive control parameters. Soft. Comput. 21(17), 5091–5102 (2017)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ170994), the National Natural Science Foundation of China (No. 61663028), the Distinguished Young Talents Plan of Jiangxi Province (No. 20171BCB23075), the Natural Science Foundation of Jiangxi Province (No. 20171BAB202035), the China Scholarship Council (No. 201608360029), and the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing (No. 2016WICSIP015).

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Hui Wang

  2. Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada

    Zhangxin Chen & Keliu Wu

  3. School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China

    Wenjun Wang

  4. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Zhijian Wu

  5. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Wei Li

Authors
  1. Hui Wang
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  2. Zhangxin Chen
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  3. Wenjun Wang
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  4. Zhijian Wu
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  5. Keliu Wu
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  6. Wei Li
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Corresponding author

Correspondence to Hui Wang .

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Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Wang, H., Chen, Z., Wang, W., Wu, Z., Wu, K., Li, W. (2018). Improving Energy Demand Estimation Using an Adaptive Firefly Algorithm. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_15

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  • DOI: https://doi.org/10.1007/978-981-13-1648-7_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1647-0

  • Online ISBN: 978-981-13-1648-7

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