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

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.