Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building

Passos, L.A. de Araujo; Ceha, Thomas Joseph; Baldi, Simone; Schutter, Bart De

doi:10.1016/j.energy.2022.126177

Cited by 16 publications

(2 citation statements)

References 37 publications

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“…Research by Rahman and Smith [120] demonstrates that machine learning predictions of fuel consumption in commercial buildings are also feasible in different climates and building types. De Araujo Passos et al [179] used surrogate models and MPC methods to optimize the energy retrofit for specific buildings and realize the maximum passive operation of the HVAC system. In the application of digital twin technology, studies by Anders Clausen, H. Hosamo Hosamo, Yasmin Fathy, and S. H. Khajavi [134,160,163,185] have shown that digital twin technology can significantly improve the energy efficiency of buildings through building modelling, energy analysis, and multi-objective optimization and efficiency and reduce energy consumption.…”

Section: Algorithms and Deep Learning For Energy Efficiencymentioning

confidence: 99%

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: Algorithms and Deep Learning For Energy Efficiencymentioning

confidence: 99%

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

Tahmasebinia,

Lin,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…One challenge is to find a suitable g nn ; it could be a linear physical model with state space representation [34], a non-linear one [35] or a grey-box model identified from data [36].One challenge is to find a suitable g nn ; it could be a linear physical model with state space representation [34] or a non-linear one [35]. PB The method explored in this work uses a black-box model following a methodology extracted from [37].…”

Section: Second Level: Dynamic Optimization Via Multi Energy-mpcmentioning

confidence: 99%

Multi-flow optimization of a greenhouse system: A hierarchical control approach

Blaud,

Haurant,

Chevrel

et al. 2023

Applied Energy

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A Study of Asymmetric Hyperbolic Heat Storage Unit

Luo,

Du,

Wang

2024

Energy Storage

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Due to the advantages of high energy density and constant temperature, phase change energy storage technology has attracted much attention in energy saving and efficient utilization of energy. In this paper, Fluent software is used to simulate and analyze the heat storage characteristics of six new thermal storage units: corrugated tube, hyperbolic‐shape, non‐hyperbolic‐shape, symmetric frustum‐shape, non‐frustum‐shape, and bow‐shape. The results show that the melting rate of the non‐hyperbolic‐shape phase change unit is significantly higher than that of the circular tube, the heat storage time is shortened by 22.52%, and Ra* reaches 0.0086. Second, the effects of the radius difference (δ) between the inlet and outlet of the non‐hyperbolic‐shape phase change unit and the asymmetric position (s) on the heat storage process are studied. The results show that when δ increases from 2 to 10 mm, the melting time of phase change material (PCM) is shortened by 22.89%, that is, the average heat transfer rate between PCM and heat transfer fluid increases with the increase of δ. On the other hand, the average heat storage rate of the heat storage unit decreases first and then increases with the increase of s. When δ = 10 mm, s = 35 mm is the best working condition, the average heat storage rate of the non‐hyperbolic‐shape thermal storage unit reaches 34.3 J/s. This study can provide new ideas and references for the optimization design of latent heat storage units and the progress of experiments.

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Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building

Cited by 16 publications

References 37 publications

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

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

Multi-flow optimization of a greenhouse system: A hierarchical control approach

A Study of Asymmetric Hyperbolic Heat Storage Unit

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