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RETRACTED ARTICLE: Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors

Author

Listed:
  • Yan Cao

    (Xi’an Technological University)

  • Towhid Pourrostam

    (Islamic Azad University)

  • Yousef Zandi

    (Ghateh Gostar Novin Company)

  • Nebojša Denić

    (University of Prishtina)

  • Bogdan Ćirković

    (University of Prishtina)

  • Alireza Sadighi Agdas

    (Ghateh Gostar Novin Company)

  • Abdellatif Selmi

    (Prince Sattam Bin Abdulaziz University
    Ecole Nationale D’Ingénieurs deTunis (ENIT))

  • Vuk Vujović

    (Alfa BK University)

  • Kittisak Jermsittiparsert

    (Duy Tan University
    Duy Tan University
    MBA School, Henan University of Economics and Law)

  • Momir Milic

    (Alfa BK University)

Abstract

Energy performance of buildings is an important issue to estimate the energy waste of buildings and their impact on the environment, so designing energy-efficient buildings could improve their energy performance. In this case, the estimation of heating and cooling loads plays an important role in this regard. However, there are few factors with unpredictable influences on the heating and cooling loads. This study has attempted to analyze the eight parameters that can significantly affect the heating and cooling loads through the Neuro-fuzzy logic approach. Accordingly, the eight parameters of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution were considered as inputs and predicting the cooling and heating load changes was regarded as the output of this study. The model was developed and its results were measured in two regression indicators of r and RMSE. Based on the obtained results it was found that roof has the strongest impact on heating and cooling loads (RMSE: 4.3596), moreover, if two factors were concurrently changed, then the combination of relative compactness and wall area can significantly affect the heating and cooling loads (RMSE: 2.6312). The most influential combination of three factors is observed as well for the heating and cooling load and these factors are relative compactness, wall area and glazing area (0.5948). The most influential combination of three factors is observed as well for the hearing load and these factors are relative compactness, wall area and glazing area (RMSE: 0.5948 and RMSE: 1.5769 for heating and cooling load, respectively). However, Neuro-fuzzy logic showed overfitting for more than two inputs, therefore it is not recommended for more than two inputs.

Suggested Citation

  • Yan Cao & Towhid Pourrostam & Yousef Zandi & Nebojša Denić & Bogdan Ćirković & Alireza Sadighi Agdas & Abdellatif Selmi & Vuk Vujović & Kittisak Jermsittiparsert & Momir Milic, 2021. "RETRACTED ARTICLE: Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17349-17373, December.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:12:d:10.1007_s10668-021-01382-4
    DOI: 10.1007/s10668-021-01382-4
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    References listed on IDEAS

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    1. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    2. I. Mansouri & M. Shariati & M. Safa & Z. Ibrahim & M. M. Tahir & D. Petković, 2019. "RETRACTED ARTICLE: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1247-1257, March.
    3. Arteconi, Alessia & Mugnini, Alice & Polonara, Fabio, 2019. "Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    5. Safa, Maryam & Sari, Puteri Azura & Shariati, Mahdi & Suhatril, Meldi & Trung, Nguyen Thoi & Wakil, Karzan & Khorami, Majid, 2020. "Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    6. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    7. Liu, Jianjun & Wu, Changzhi & Wu, Guoning & Wang, Xiangyu, 2015. "A novel differential search algorithm and applications for structure design," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 246-269.
    8. Long, Qiang & Wu, Changzhi & Wang, Xiangyu, 2015. "A system of nonsmooth equations solver based upon subgradient method," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 284-299.
    9. Cungen Liu & Huanqing Wang & Xiaoping Liu & Yucheng Zhou, 2020. "Adaptive fuzzy funnel control for nonlinear systems with input deadzone and saturation," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(9), pages 1542-1555, July.
    10. 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.
    11. Jianbo Zhu & Qianqian Shi & Peng Wu & Zhaohan Sheng & Xiangyu Wang, 2018. "Complexity Analysis of Prefabrication Contractors’ Dynamic Price Competition in Mega Projects with Different Competition Strategies," Complexity, Hindawi, vol. 2018, pages 1-9, September.
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