Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

Year 2020, Volume: 33 Issue: 1, 62 - 72, 01.03.2020

Ali Danandeh Mehr , Farzaneh Bagheri Mir Jafar Sadegh Safari

https://doi.org/10.35378/gujs.554463

Cited By: 2

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.

Keywords

Genetic programing, Decision tree, Electricity demand, Nicosia

References

• Aghaei, J., & Alizadeh, M. I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64-72.
• Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and management, 49(8), 2272-2278.
• Bakhshaii, A., & Stull, R. (2012). Electric load forecasting for western Canada: A comparison of two non-linear methods. Atmosphere-Ocean, 50(3), 352-363.
• Balk, B., & Elder, K. (2000). Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed. Water Resources Research, 36(1), 13-26.
• Bhattacharya, M., Abraham, A., & Nath, B. (2002). A linear genetic programming approach for modelling electricity demand prediction in Victoria. In Hybrid Information Systems, 379-393. Physica, Heidelberg, Berlin, Germany Springer-Verlag.
• Breiman, L., J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, Wadsworth, Belmont, Calif., 1984.
• Çunkaş, M., & Taşkiran, U. (2011). Turkey's electricity consumption forecasting using genetic programming. Energy Sources, Part B: Economics, Planning, and Policy, 6(4), 406-416.
• Danandeh Mehr A., Bagheri, F., & Reşatoğlu, R. (2018a) “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.
• Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M., & Yaseen, Z. M. (2018b). Genetic programming in water resources engineering: A state-of-the-art review. Journal of Hydrology 566, 643-667.
• Danandeh Mehr, A., Nourani, V., Hrnjica, B., & Molajou, A. (2017). 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.
• Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517.
• Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
• Hrnjica, B., & Danandeh Mehr, A. (2019). Optimized Genetic Programming Applications: Emerging Research and Opportunities: Emerging Research and Opportunities. Hershey, PA, USA, IGI-Global.
• Mehrotra, K., Mohan, C. K., & Ranka, S. (2000). Elements of artificial neural networks. 2nd ed. Massachusetts, USA, MIT press.
• Mousavi, S. M., Mostafavi, E. S., & Hosseinpour, F. (2014). Gene expression programming as a basis for new generation of electricity demand prediction models. Computers & Industrial Engineering, 74, 120-128.
• Mwasilu, F., Justo, J. J., Kim, E. K., Do, T. D., & Jung, J. W. (2014). 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.
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
• Safari, M. J. S. (2019). DT, GR and MARS models for sediment transport in sewer pipes. Water Science and Technology https://doi.org/10.2166/wst.2019.106.
• Tso, G. K., & Yau, K. K. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761-1768.
• Vaheddoost, B., Aksoy, H., Abghari, H., & Naghadeh, S. (2015). 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 (pp. 5-7).
• Yu, Z., Haghighat, F., Fung, B. C., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637-1646.