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Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Author

Listed:
  • Hossein Moayedi

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    Institute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, Germany
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

Abstract

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1649-:d:517853
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    1. Ghahramani, Ali & Zhang, Kenan & Dutta, Kanu & Yang, Zheng & Becerik-Gerber, Burcin, 2016. "Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings," Applied Energy, Elsevier, vol. 165(C), pages 930-942.
    2. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    3. Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
    4. Ihara, Takeshi & Gustavsen, Arild & Jelle, Bjørn Petter, 2015. "Effect of facade components on energy efficiency in office buildings," Applied Energy, Elsevier, vol. 158(C), pages 422-432.
    5. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
    6. Chen, Huazhou & Chen, An & Xu, Lili & Xie, Hai & Qiao, Hanli & Lin, Qinyong & Cai, Ken, 2020. "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources," Agricultural Water Management, Elsevier, vol. 240(C).
    7. Li, Zhi-Guo & Cheng, Han & Gu, Tian-Yao, 2019. "Research on dynamic relationship between natural gas consumption and economic growth in China," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 334-339.
    8. 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.
    9. 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.
    10. Lu, Hongwei & Tian, Peipei & He, Li, 2019. "Evaluating the global potential of aquifer thermal energy storage and determining the potential worldwide hotspots driven by socio-economic, geo-hydrologic and climatic conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 788-796.
    11. Akemi Gálvez & Andrés Iglesias, 2013. "Firefly Algorithm for Polynomial Bézier Surface Parameterization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-9, September.
    12. Huang, Yu & Niu, Jian-lei & Chung, Tse-ming, 2014. "Comprehensive analysis on thermal and daylighting performance of glazing and shading designs on office building envelope in cooling-dominant climates," Applied Energy, Elsevier, vol. 134(C), pages 215-228.
    13. Fu, Xiuwen & Yang, Yongsheng, 2020. "Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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