Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings

Faisal Aqlan, Abdulaziz Ahmed, Krishnaswami Srihari, Mohammad T. Khasawneh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Energy consumption of buildings worldwide has steadily increased over the past couple of decades. Furthermore, energy performance of buildings is one of the factors that contribute to energy waste and its perennial adverse impact on the environment. This paper presents a data mining approach for assessing the heating and cooling requirements of residential buildings. The proposed approach combines Artificial Neural Networks (ANNs) and cluster analysis to assess and predict the heating and cooling energy efficiency of residential buildings. The ANN-based model uses eight input variables (i.e., relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution) to predict both the heating and cooling loads of residential buildings. Buildings are then clustered based on the output variables using the K-means clustering method. The proposed approach is used to assess and evaluate 768 diverse residential buildings based on simulated literature data. The research results showed that the proposed approach can effectively predict the heating and cooling requirements of residential buildings based on the input variables considered with a very high level of accuracy.

Original languageEnglish (US)
Title of host publicationIIE Annual Conference and Expo 2014
PublisherInstitute of Industrial Engineers
Pages3936-3943
Number of pages8
ISBN (Electronic)9780983762430
StatePublished - Jan 1 2014
EventIIE Annual Conference and Expo 2014 - Montreal, Canada
Duration: May 31 2014Jun 3 2014

Publication series

NameIIE Annual Conference and Expo 2014

Other

OtherIIE Annual Conference and Expo 2014
CountryCanada
CityMontreal
Period5/31/146/3/14

Fingerprint

Cluster analysis
Electric network analysis
Energy efficiency
Neural networks
Cooling
Heating
Roofs
Data mining
Loads (forces)
Energy utilization

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering

Cite this

Aqlan, F., Ahmed, A., Srihari, K., & Khasawneh, M. T. (2014). Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings. In IIE Annual Conference and Expo 2014 (pp. 3936-3943). (IIE Annual Conference and Expo 2014). Institute of Industrial Engineers.
Aqlan, Faisal ; Ahmed, Abdulaziz ; Srihari, Krishnaswami ; Khasawneh, Mohammad T. / Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings. IIE Annual Conference and Expo 2014. Institute of Industrial Engineers, 2014. pp. 3936-3943 (IIE Annual Conference and Expo 2014).
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Aqlan, F, Ahmed, A, Srihari, K & Khasawneh, MT 2014, Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings. in IIE Annual Conference and Expo 2014. IIE Annual Conference and Expo 2014, Institute of Industrial Engineers, pp. 3936-3943, IIE Annual Conference and Expo 2014, Montreal, Canada, 5/31/14.

Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings. / Aqlan, Faisal; Ahmed, Abdulaziz; Srihari, Krishnaswami; Khasawneh, Mohammad T.

IIE Annual Conference and Expo 2014. Institute of Industrial Engineers, 2014. p. 3936-3943 (IIE Annual Conference and Expo 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Ahmed, Abdulaziz

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AB - Energy consumption of buildings worldwide has steadily increased over the past couple of decades. Furthermore, energy performance of buildings is one of the factors that contribute to energy waste and its perennial adverse impact on the environment. This paper presents a data mining approach for assessing the heating and cooling requirements of residential buildings. The proposed approach combines Artificial Neural Networks (ANNs) and cluster analysis to assess and predict the heating and cooling energy efficiency of residential buildings. The ANN-based model uses eight input variables (i.e., relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution) to predict both the heating and cooling loads of residential buildings. Buildings are then clustered based on the output variables using the K-means clustering method. The proposed approach is used to assess and evaluate 768 diverse residential buildings based on simulated literature data. The research results showed that the proposed approach can effectively predict the heating and cooling requirements of residential buildings based on the input variables considered with a very high level of accuracy.

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Aqlan F, Ahmed A, Srihari K, Khasawneh MT. Integrating artificial neural networks and cluster analysis to assess energy efficiency of buildings. In IIE Annual Conference and Expo 2014. Institute of Industrial Engineers. 2014. p. 3936-3943. (IIE Annual Conference and Expo 2014).