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Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions

Author

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  • Qi Dong

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Heilongjiang Cold Region Architectural Science Key Laboratory, Harbin 150001, China)

  • Kai Xing

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Heilongjiang Cold Region Architectural Science Key Laboratory, Harbin 150001, China)

  • Hongrui Zhang

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Heilongjiang Cold Region Architectural Science Key Laboratory, Harbin 150001, China)

Abstract

This paper aims to develop an artificial neural network (ANN) to predict the energy consumption and cost of cross laminated timber (CLT) office buildings in severe cold regions during the early stage of architectural design. Eleven variables were selected as input variables including building form and construction variables, and the values of input variables were determined by local building standards and surveys. ANNs were trained by the simulation data and Latin hypercube sampling (LHS) method was used to select training datasets for the ANN training. The best ANN was obtained by analyzing the output variables and the number of hidden layer neurons. The results showed that the ANN with multiple outputs presented better prediction performance than the ANN with single output. Moreover, the number of hidden layer neurons in ANN should be greater than five and preferably 10, and the best mean square error (MSE) value was 1.957 × 10 3 . In addition, it was found that the time of predicting building energy consumption and cost by ANN was 80% shorter than that of traditional building energy consumption simulation and cost calculation method.

Suggested Citation

  • Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2017:i:1:p:84-:d:124880
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    References listed on IDEAS

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    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    2. Urge-Vorsatz, Diana & Novikova, Aleksandra, 2008. "Potentials and costs of carbon dioxide mitigation in the world's buildings," Energy Policy, Elsevier, vol. 36(2), pages 642-661, February.
    3. Haibo Guo & Ying Liu & Wen-Shao Chang & Yu Shao & Cheng Sun, 2017. "Energy Saving and Carbon Reduction in the Operation Stage of Cross Laminated Timber Residential Buildings in China," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    4. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
    5. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    6. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    7. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    8. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    9. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    10. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    11. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    12. Sadineni, Suresh B. & Madala, Srikanth & Boehm, Robert F., 2011. "Passive building energy savings: A review of building envelope components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3617-3631.
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    Cited by:

    1. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Jozef Švajlenka & Mária Kozlovská & Miroslav Badida & Marek Moravec & Tibor Dzuro & František Vranay, 2020. "Analysis of the Characteristics of External Walls of Wooden Prefab Cross Laminated Timber," Energies, MDPI, vol. 13(22), pages 1-14, November.
    3. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    4. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    5. Bin Xu & Xiang Yuan, 2022. "A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    6. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    7. Jozef Švajlenka & Mária Kozlovská & František Vranay & Terézia Pošiváková & Miroslava Jámborová, 2020. "Comparison of Laboratory and Computational Models of Selected Thermal-Technical Properties of Constructions Systems Based on Wood," Energies, MDPI, vol. 13(12), pages 1-15, June.
    8. Silvia Cesari & Paolo Valdiserri & Maddalena Coccagna & Sante Mazzacane, 2020. "The Energy Saving Potential of Wide Windows in Hospital Patient Rooms, Optimizing the Type of Glazing and Lighting Control Strategy under Different Climatic Conditions," Energies, MDPI, vol. 13(8), pages 1-24, April.

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