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)


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.


Genetic programming Electricity demand Time series analysis 


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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

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