Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree
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Date
2020
Authors
Ali Danandeh Mehr
Farzaneh Bagheri
Mir Jafar Sadegh Safari
Journal Title
Journal ISSN
Volume Title
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Open Access Color
GOLD
Green Open Access
Yes
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No
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Average
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Average
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Top 10%
Abstract
Several 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.
Description
Keywords
İktisat, Genetic programing;Decision tree;Electricity demand;Nicosia, Engineering, Mühendislik
Fields of Science
0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
[1] Han J. Pei J. and Kamber M. “Data mining: concepts and techniques” Elsevier. (2011).[2] Tso G. K. and Yau K. K. “Predicting electricity energy consumption: A comparison of regression analysis decision tree and neural networks” Energy 32(9): 1761-1768 (2007).[3] Ekonomou L. “Greek long-term energy consumption prediction using artificial neural networks” Energy 35(2): 512-517 (2010).[4] Yu Z. Haghighat F. Fung B. C. and Yoshino H. “A decision tree method for building energy demand modeling” Energy and Buildings 42(10): 1637-1646 (2010).[5] Azadeh A. Ghaderi S. F. and Sohrabkhani S. “Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors” Energy Conversion and management 49(8): 2272-2278 (2008).[6] Bhattacharya M. Abraham A. Nath B. “A linear genetic programming approach for modelling electricity demand prediction in Victoria” In Hybrid Information Systems 379-393. Physica Heidelberg Berlin Germany Springer-Verlag (2002). [7] Bakhshaii A. Stull R. “Electric load forecasting for western Canada: A comparison of two non-linear methods” Atmosphere-Ocean 50(3): 352-363 (2012).[8] Çunkaş M. Taşkiran U. “Turkey's electricity consumption forecasting using genetic programming” Energy Sources Part B: Economics Planning and Policy 6(4): 406-416 (2011).[9] Mousavi S. M. Mostafavi E. S. and Hosseinpour F. “Gene expression programming as a basis for new generation of electricity demand prediction models” Computers & Industrial Engineering 74: 120-128 (2014).[10] Aghaei J. and Alizadeh M. I. “Demand response in smart electricity grids equipped with renewable energy sources: A review” Renewable and Sustainable Energy Reviews 18: 64-72 (2013).[11] Mwasilu F. Justo J. J. Kim E. K. Do T. D. Jung J. W. “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration” Renewable and sustainable energy reviews 34: 501-516 (2014).[12] Mehrotra K. Mohan C. K. Ranka S. Elements of artificial neural networks. 2nd ed. Massachusetts USA MIT press (2000).[13] Danandeh Mehr A. Bagheri F. and Reşatoğlu R. “A genetic programming approach to forecast daily electricity demand” 13th International Conference on Theory and Applications of Fuzzy Systems and Soft Computing. Warsaw Poland 27–28 August (2018).[14] Quinlan J. R. Induction of decision trees. Machine Learning 1: 81–106 (1986).[15] Safari M. J. S. “DT GR and MARS models for sediment transport in sewer pipes” Water Science and Technology https://doi.org/10.2166/wst.2019.106 (2019).[16] Vaheddoost B. Aksoy H. Abghari H. Naghadeh S. “Decision tree for measuring the interaction of hyper-saline Lake and coastal aquifer in Lake Urmia.” In Proceeding of Environmental and Water Resource Institute (EWRI): Watershed Management Symposium August (5-7) (2015).[17] Breiman L. J. Friedman Olshen R. C. Stone Classification and Regression Trees Wadsworth Belmont Calif. (1984).[18] Balk B. and Elder K. “Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed” Water Resources Research 36(1): 13-26 (2000).[19] Hrnjica B. and Danandeh Mehr A. Optimized Genetic Programming Applications: Emerging Research and Opportunities: Emerging Research and Opportunities. Hershey PA USA IGI-Global (2019).[20] Danandeh Mehr A. Nourani V. Kahya E. Hrnjica B. Sattar A. M. Yaseen Z. M “Genetic programming in water resources engineering: A state-of-the-art review” Journal of Hydrology 566: 643-667 (2018).[21] 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.” International Journal of Sediment Research 33(3): 262-270 (2018).[22] 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” Journal of Hydrology 555: 397-406 (2017).[23] Danandeh Mehr A. Jabarnejad M. Nourani V. “Pareto-optimal MPSA-MGGP: A new geneannealing model for monthly rainfall forecasting” Journal of Hydrology 571: 406-415 (2019).[24] Hrnjica B. Danandeh Mehr. A. “Energy Demand Forecasting Using Deep Learning.” Smart Cities Performability Cognition & Security. Springer Cham 71-104 (2020).
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OpenCitations Citation Count
5
Source
Gazi University Journal of Science
Volume
33
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Start Page
62
End Page
72
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Scopus : 5
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