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Application of adaptive neuro-fuzzy methodology for estimating building energy consumption

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  • Naji, Sareh
  • Shamshirband, Shahaboddin
  • Basser, Hossein
  • Keivani, Afram
  • Alengaram, U. Johnson
  • Jumaat, Mohd Zamin
  • Petković, Dalibor

Abstract

The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.

Suggested Citation

  • Naji, Sareh & Shamshirband, Shahaboddin & Basser, Hossein & Keivani, Afram & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Petković, Dalibor, 2016. "Application of adaptive neuro-fuzzy methodology for estimating building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1520-1528.
    • Handle: RePEc:eee:rensus:v:53:y:2016:i:c:p:1520-1528
    DOI: 10.1016/j.rser.2015.09.062
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    References listed on IDEAS

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    Cited by:

    1. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    3. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    4. Yao Xu & Liang Sun & Bo Wang & Shanmin Ding & Xichen Ge & Shuangrong Cai, 2023. "Research on the Impact of Carbon Emissions and Spatial Form of Town Construction Land: A Study of Macheng, China," Land, MDPI, vol. 12(7), pages 1-23, July.
    5. Fu, Xueqian & Zhang, Xiurong, 2019. "Estimation of building energy consumption using weather information derived from photovoltaic power plants," Renewable Energy, Elsevier, vol. 130(C), pages 130-138.
    6. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    7. Tanmoy Chakraborty & Payel Ghosh & Satadal Mal & Utpal Biswas, 2019. "A modelling applied to active renewable energy for an existing building of higher educational institution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1361-1368, October.
    8. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
    9. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    10. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    11. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    12. Hossein Moayedi & Amir Mosavi, 2021. "Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings," Energies, MDPI, vol. 14(6), pages 1-19, March.

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