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

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  • Tso, Geoffrey K.F.
  • Yau, Kelvin K.W.

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.

Suggested Citation

  • Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:9:p:1761-1768
    DOI: 10.1016/j.energy.2006.11.010
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