Advertisement

A Genetic Programming Approach to Forecast Daily Electricity Demand

  • Ali Danandeh Mehr
  • Farzaneh Bagheri
  • Rifat ReşatoğluEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

A number of recent researches have compared machine learning techniques to find more reliable approaches to solve variety of engineering problems. In the present study, capability of canonical genetic programming (GP) technique to model daily electrical energy consumption (ED) as an alternative for electrical demand prediction was investigated. For this aim, using the most recent ED data recorded at northern part of Nicosia, Cyprus, we put forward two daily prediction scenarios subjected to train and validate by GPdotNET, an open source GP software. Minimizing root mean square error between the modeled and observed data as the objective function, the best prediction model at each scenario has been presented for the city. The results indicated the promising role of GP for daily ED prediction in Nicosia, however it suffers from lagged prediction that must be considered in practical application.

Keywords

Genetic programming Electricity demand Time series analysis 

References

  1. Al-Musaylh, M.S., Deo, R.C., et al.: Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Adv. Eng. Inform. 35, 1–16 (2018)CrossRefGoogle Scholar
  2. Amjadi, M.H., Nezamabadi-pour, H., Farsangi, M.M.: Estimation of electricity demand of Iran using two heuristic algorithms. Energy Convers. Manag. 51(3), 493 (2010)CrossRefGoogle Scholar
  3. Azadeh, A., Ghaderi, S.F., Sohrabkhani, S.: Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers. Manag. 49, 2272–2278 (2008)CrossRefGoogle Scholar
  4. Bakhshaii, A., Stull, R.: Electric load forecasting for Western Canada: a comparison of two non-linear methods. Atmos.-Ocean. 50(3), 352–363 (2012)CrossRefGoogle Scholar
  5. Bhattacharya, M., Abraham, A., Nath, B.: A linear genetic programming approach for modeling electricity demand prediction in Victoria. In: Abraham, A., Köppen, M. (eds.) Hybrid Information Systems (Advances in Soft Computing), p. 734. Springer, Heidelberg (2002).  https://doi.org/10.1007/978-3-7908-1782-9_28CrossRefGoogle Scholar
  6. Çunkaş, M., Taşkıran, U.: Turkey’s electricity consumption forecasting using genetic programming. Energy Sources Part B: Econ. Plan. Policy 6(4), 406–416 (2011)CrossRefGoogle Scholar
  7. Danandeh Mehr, A., Nourani, V., Hrnjica, B., Molajou, A.: A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events. J. Hydrol. 555, 397–506 (2017)CrossRefGoogle Scholar
  8. Danandeh Mehr, A., Nourani, V.: Season algorithm-multigene genetic programming: a new approach for rainfall-runoff modelling. Water Resour. Manag. 32(8), 2665–2679 (2018)CrossRefGoogle Scholar
  9. Danandeh Mehr, A., Nourani, V., Khosrowshahi, V.K., Ghorbani, M.A.: A hybrid support vector regression–firefly model for monthly rainfall forecasting. Int. J. Environ. Sci. Technol., 1–12 (2018)Google Scholar
  10. Ekonomou, L.: Greek long-term energy consumption prediction using artificial neural networks. Energy 35, 512–517 (2010)CrossRefGoogle Scholar
  11. Fan, S., Chen, L.: Short-term load forecasting based on an adaptive hybrid method. IEEE Trans. Power Syst. 21, 392–401 (2006)CrossRefGoogle Scholar
  12. Forouzanfar, M., Doustmohammadi, A., Hasanzadeh, S., Shakouri, H.G.: Transport energy demand forecast using multi-level genetic programming. Appl. Energy 91(1), 496–503 (2012)CrossRefGoogle Scholar
  13. Hrnjica, B., Danandeh Mehr, A.: Optimized Genetic Programming Applications: Emerging Research and Opportunities. IGI-Global (2019). ISBN 13:9781522560050,  https://doi.org/10.4018/978-1-5225-6005-0
  14. Huo, L., Fan, X., Xie, Y., Yin, J.: Short-term load forecasting based 440 on the method of genetic programming. IEEE International Conference on Mechatronics and Automation, Harbin, China, 5–8 August, pp. 839–843 (2007)Google Scholar
  15. Karabulut, K., Alkan, A., Yılmaz, A.S.: Long term energy consumption forecasting using genetic programming. Math. Comput. Appl. 13, 71–80 (2008)Google Scholar
  16. Kumar, B., Jha, A., Deshpande, V., Sreenivasulu, G.: Regression model for sediment transport problems using multi-gene symbolic genetic programming. Comput. Electron. Agric. 103, 82–90 (2014)CrossRefGoogle Scholar
  17. Mousavi, S.M., Mostafavi, E.S., Hosseinpour, F.: Gene expression programming as a basis for new generation of electricity demand prediction models. Comput. Ind. Eng. 74, 120–128 (2014)CrossRefGoogle Scholar
  18. Nagbe, K., Cugliari, J., Jacques, J.: Electricity Demand Forecasting Using a Functional State Space Model (2018)Google Scholar
  19. State Planning Organization, General population and housing unit census. Statistical Yearbook, 2015. Nicosia, Turkish Republic of Northern Cyprus (2011)Google Scholar
  20. Safari, M.J.S., Danandeh Mehr, A.: Multigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed deposit. Int. J. Sedim. Res. 33(3), 262–270 (2018)CrossRefGoogle Scholar
  21. Tso, G.K.F., Yau, K.K.W.: Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural network. Energy 32(9), 1761–1768 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Danandeh Mehr
    • 1
  • Farzaneh Bagheri
    • 2
  • Rifat Reşatoğlu
    • 3
    Email author
  1. 1.Civil Engineering DepartmentAntalya Bilim UniversityAntalyaTurkey
  2. 2.Department of Electrical and Electronic EngineeringEastern Mediterranean UniversityFamagusta, Mersin 10Turkey
  3. 3.Faculty of Civil and Environmental EngineeringNear East UniversityNicosia, Mersin 10Turkey

Personalised recommendations