Retraction Note: Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters

Naji, Sareh; Shamshirband, Shahaboddin; Basser, Hamed; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Amirmojahedi, Mohsen

doi:10.1007/s12053-018-9761-2

Cited by 1 publication

(1 citation statement)

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“…Hsu (2015) used integrated clustering methods for predicting energy consumption of buildings, using cluster wise regression, k-means and model-based clustering. Naji et al (2016) used several machine learning methods, such as support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and applied them in Energy Plus simulation program to predict energy consumption of residential buildings. However, the efficiency of machine learning methods integration in this area is still not investigated enough.…”

Section: Literature Reviewmentioning

confidence: 99%

Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

Zekić–Sušac

Scitovski

Has

2018

Croatian Review of Economic, Business and Social Statistics

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Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector.

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Section: Literature Reviewmentioning

confidence: 99%