Ali Danandeh MehrFarzaneh BagheriMir Jafar Sadegh Safari2025-10-06202013039709, 214717622147-176210.35378/gujs.554463https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086766157&doi=10.35378%2Fgujs.554463&partnerID=40&md5=8bbb6f98f8c29a91b752a0269133052ehttps://gcris.yasar.edu.tr/handle/123456789/9299Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper for the first time compares the efficiency of the decision tree (DT) and classic genetic programming (GP) data mining models developed for electrical energy demand forecasting in Nicosia Northern Cyprus. The models were trained and tested using daily electricity consumptions measured during the period 2011-2016 and were compared in terms of three statistical performance indices including coefficient of determination mean absolute percentage error and concordance coefficient. The prediction results showed that the proposed models can be effectively applied to forecasts of electrical energy demand. The results also indicated that the GP is slightly superior to DT in terms of the performance indices. © 2023 Elsevier B.V. All rights reserved.EnglishDecision Tree, Electricity Demand, Genetic Programing, Nicosia, Data Mining, Electric Power Utilization, Forecasting, Genetic Algorithms, Genetic Programming, Optimal Systems, Data Mining Models, Data-mining Techniques, Demand Forecast, Electrical Energy Demand, Electricity Demands, Energy Demand Forecasting, Energy Demand Prediction, Genetic Programing, Nicosium, Performance Indices, Decision TreesData mining, Electric power utilization, Forecasting, Genetic algorithms, Genetic programming, Optimal systems, Data mining models, Data-mining techniques, Demand forecast, Electrical energy demand, Electricity demands, Energy demand forecasting, Energy demand prediction, Genetic programing, Nicosium, Performance indices, Decision treesArticle