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Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey

  • Zahra Pezeshki
  • Sayyed Majid Mazinani
Article
  • 159 Downloads

Abstract

Data Mining (DM) is a useful technique to discover useful patterns which lead to large searches. This method offers a reliable treatment of all developmental phases from problem and data understanding through data preprocessing to deployment of the results. DM plays an important role in energy efficiency. The construction industry has numerous sources information to compare and turn them into beneficial information. Artificial neural networks (ANN), fuzzy logic (FL) and neuro fuzzy (NF) are used techniques. Although the ANN and FL have many advantages, they also have certain defects. NF enjoys the advantages of both ANN and FL. In this paper, by comparing these techniques present in articles from 2009 to 2017, we have introduced four advantages for NF technique and indicated that the second advantage has been paid less attention other ones. The results reveal that the NF method is more successful than FL and ANN for predicting the thermal efficiency of buildings. However, NF with a learning phase proves to be computationally heavy and time-consuming, especially when the number of rules, the antecedents and the model delays are high. As a result, the proposed method, using nonlinear neural Model Predictive Controllers, is the best answer to thermal control strategies.

Keywords

Energy efficiency Thermal consumption Construction industry Artificial neural networks Data Mining Fuzzy logic Neuro fuzzy 

Notes

Acknowledgements

The authors wish to express sincere gratitude to the anonymous reviewers for their constructive comments and helpful suggestions, which lead to substantial improvements of this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electrical and Robotic EngineeringShahrood University of TechnologyShahrudIran
  2. 2.Electrical Department, Faculty of EngineeringImam Reza International UniversityMashhadIran

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