Energy Performance of a Service Building: Comparison Between EnergyPlus and Revit

Silva, João; Brás, José; Noversa, Ricardo; Rodrigues, Nélson; Martins, Luís Barreiros; Teixeira, José Carlos; Teixeira, Senhorinha

doi:10.1007/978-3-030-58817-5_16

Cited by 4 publications

(2 citation statements)

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“…Simulation of the field of service equipment in a building using Energy Plus includes an in-depth analysis of the energy performance of a service building in the district of Aveiro, Portugal by Silva et al [51] The article analyzes in detail the building's characteristics, equipment, and HVAC systems. It reveals that lighting and air conditioning are the main sources of energy consumption.…”

Section: Software For Energy Prediction-actual Software Casesmentioning

confidence: 99%

“…For example, in-depth studies of the calorific value of building structures [64], human actions such as improper temperature set points, and improper HVAC system design and operation [75]. In using tools such as Revit, for example, the HVAC system cannot be created, and an existing HVAC system needs to be selected from the software library [51].…”

Section: Bem Softwarementioning

confidence: 99%

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Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy

Tahmasebinia,

Lin,

et al. 2023

Applied Sciences

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Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals.

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Section: Software For Energy Prediction-actual Software Casesmentioning

confidence: 99%