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Automated Building Energy Modeling and Assessment Tool (ABEMAT)

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Listed:
  • Kamel, Ehsan
  • Memari, Ali M.

Abstract

Buildings contribute to about 40% of total annual energy consumption in the U.S. and saving a small percentage of that can lead to significant economic and environmental impacts. Building Energy Modeling (BEM) tools are important in reducing building's energy consumption. Most of the available tools only provide total energy consumption related to HVAC system, lighting, hot water, and appliances. While fine-grained data may be available in some computer tools, the process for obtaining these data is time-consuming, error-prone, and requires software-related skills.

Suggested Citation

  • Kamel, Ehsan & Memari, Ali M., 2018. "Automated Building Energy Modeling and Assessment Tool (ABEMAT)," Energy, Elsevier, vol. 147(C), pages 15-24.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:15-24
    DOI: 10.1016/j.energy.2018.01.023
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    2. Suzana Domjan & Sašo Medved & Boštjan Černe & Ciril Arkar, 2019. "Fast Modelling of nZEB Metrics of Office Buildings Built with Advanced Glass and BIPV Facade Structures," Energies, MDPI, vol. 12(16), pages 1-18, August.
    3. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    4. José Miguel Márquez-Martinón & Norena Martín-Dorta & Eduardo González-Díaz & Benjamín González-Díaz, 2021. "Influence of Thermal Enclosures on Energy Saving Simulations of Residential Building Typologies in European Climatic Zones," Sustainability, MDPI, vol. 13(15), pages 1-19, August.
    5. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    6. 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.
    7. Shabir Ahmad & Sehrish Malik & Israr Ullah & Dong-Hwan Park & Kwangsoo Kim & DoHyeun Kim, 2019. "Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
    8. Ngoc-Son Truong & Duc Long Luong & Quang Trung Nguyen, 2023. "BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness," Energies, MDPI, vol. 16(10), pages 1-24, May.

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