Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

Wang, Hao; Chen, Xiwen; Vital, Natan; Duffy, Edward; Razi, Abolfazl

doi:10.1016/j.apenergy.2023.122354

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…For conducting BPMP analysis, based on simulation experience and studies by other researchers (Wang et al, 2024;Deng et al, 2023;Chen et al, 2023), OpenStudio (OpenStudio, 2024 was used in this study. OpenStudio is an open-source software platform designed to facilitate the energy modeling and analysis of building designs.…”

Section: Openstudio As Bpmpmentioning

confidence: 99%

Optimizing office building operations: a framework for continuous dynamic energy simulations in decision-making for efficiency

Borodinecs,

Palcikovskis,

Krumins

et al. 2024

Front. Built Environ.

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Digital twins represent a promising approach for sustainable building operations and management in the context of the carbon neutrality goals of the European Union (EU). Using OpenStudio, an opensource platform for building energy modeling, we demonstrated the creation and editing of building digital twins. OpenStudio provides a user-friendly interface and extensive simulation capabilities, allowing detailed and accurate modeling of building components and systems. Using OpenStudio Measures, users can automate tasks and customize simulation models to optimize the building performance. The process of creating a building digital twin involves collecting historical data and accurately representing the building geometry; materials; schedules; and heating, ventilation, and air conditioning (HVAC) systems. Challenges such as data availability and model accuracy highlight the importance of modeling practices. Editing the digital twin involves modifying the OpenStudio model files and EnergyPlus weather files to simulate different building operation scenarios. Python programming language opportunities were considered for digital twin file modification. The potential of digital twins lies in their ability to simulate future building conditions and optimize building system settings. By integrating digital twins with machine learning algorithms and connecting them directly to building management systems, optimal building control strategies can be automated, thereby reducing energy consumption and improving occupant comfort levels.

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Section: Openstudio As Bpmpmentioning

confidence: 99%