Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

Geoffrey K.F. Tso and Kelvin K.W. Yau

Energy, 2007, vol. 32, issue 9, 1761-1768

Abstract: This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical application to an electricity energy consumption study, the decision tree and neural network models appear to be viable alternatives to the stepwise regression model in understanding energy consumption patterns and predicting energy consumption levels. With the emergence of the data mining approach for predictive modeling, different types of models can be built in a unified platform: to implement various modeling techniques, assess the performance of different models and select the most appropriate model for future prediction.

Keywords: Data mining; Decision tree; Electricity energy consumption; Neural networks; Regression analysis (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (128)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:32:y:2007:i:9:p:1761-1768

DOI: 10.1016/j.energy.2006.11.010

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