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A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

Author

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  • Guofeng Ma

    (School of Economics and Management, Tongji University, Shanghai 20092, China)

  • Ying Liu

    (School of Economics and Management, Tongji University, Shanghai 20092, China)

  • Shanshan Shang

    (International Business Administration Academic, Shanghai International Studies University, Shanghai 201620, China)

Abstract

It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.

Suggested Citation

  • Guofeng Ma & Ying Liu & Shanshan Shang, 2019. "A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4972-:d:266364
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    References listed on IDEAS

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    1. Anupama Sharma & Richa Tiwari, 2007. "Evaluation of data for developing an adaptive model of thermal comfort and preference," Environment Systems and Decisions, Springer, vol. 27(1), pages 73-81, March.
    2. Sungwoo Lee & Sungho Tae & Seungjun Roh & Taehyung Kim, 2015. "Green Template for Life Cycle Assessment of Buildings Based on Building Information Modeling: Focus on Embodied Environmental Impact," Sustainability, MDPI, vol. 7(12), pages 1-15, December.
    3. Jin-Up Kim & Oussama A. Hadadi & Hyunjoo Kim & Jonghyeob Kim, 2018. "Development of A BIM-Based Maintenance Decision-Making Framework for the Optimization between Energy Efficiency and Investment Costs," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    4. von Grabe, Jörn, 2016. "Potential of artificial neural networks to predict thermal sensation votes," Applied Energy, Elsevier, vol. 161(C), pages 412-424.
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    Cited by:

    1. Jungsik Choi & Sejin Lee, 2023. "A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    2. Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    3. Linyan Chen & Xin Gao & Shitao Gong & Zhou Li, 2020. "Regionalization of Green Building Development in China: A Comprehensive Evaluation Model Based on the Catastrophe Progression Method," Sustainability, MDPI, vol. 12(15), pages 1-22, July.
    4. Abdelali Agouzoul & Emmanuel Simeu & Mohamed Tabaa, 2024. "Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency," Sustainability, MDPI, vol. 16(7), pages 1-29, March.
    5. Mikhail Demianenko & Carlo Iapige De Gaetani, 2021. "A Procedure for Automating Energy Analyses in the BIM Context Exploiting Artificial Neural Networks and Transfer Learning Technique," Energies, MDPI, vol. 14(10), pages 1-18, May.

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