Model Predictive Control for Smart Buildings to Provide the Demand Side Flexibility in the Multi-Carrier Energy Context: Current Status, Pros and Cons, Feasibility and Barriers

Zong, Yun; Wen-jing, SU; Wang, Jiawei; Rodek, Jakub Krzysztof; Jiang, Chuhao; Christensen, Morten Herget; You, Shi; Zhou, You; Mu, Shujun

doi:10.1016/j.egypro.2019.01.981

Cited by 20 publications

(8 citation statements)

References 2 publications

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“…However, implementing sustainable energy solutions for the future power system with a high penetration of RES is a critical challenge of the present. Due to the intermittent nature of RES, it is imperative to ensure their market availability to satisfy demand through the development of smart and dynamic energy systems which can integrate the fluctuating RES into thermal grids to generate flexibility on the demand side through utilizing various Thermal Energy Storage (TES) capabilities [1].…”

Section: A Motivation and Backgroundmentioning

confidence: 99%

Enhancing demand-side flexibility for Heat Booster Substations in Ultra-Low Temperature District Heating systems

Nunna

Zong

Thorsen

2022

2022 International Conference on Smart Energy Systems and Technologies (SEST)

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Section: A Motivation and Backgroundmentioning

confidence: 99%

Enhancing demand-side flexibility for Heat Booster Substations in Ultra-Low Temperature District Heating systems

Nunna

Zong

Thorsen

2022

2022 International Conference on Smart Energy Systems and Technologies (SEST)

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“…In the process of defining smart buildings and BIM, this paper summarizes the relevant keywords of two definitions: (1) "smart buildings" [12], "smart building * " [20,21], and "Smart construction" [22]; (2) "BIM" [23] and "building information model * " [17,[24][25][26]. ese keywords are used because they have similar meanings.…”

Section: Concepts Of Smart Buildings and Bimmentioning

confidence: 99%

Adopting Building Information Modeling (BIM) for the Development of Smart Buildings: A Review of Enabling Applications and Challenges

Yang

Han

Zeng

et al. 2021

Advances in Civil Engineering

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The construction industry is undergoing a digital revolution due to the emergence of new technologies. A significant trend is that construction projects have been transformed and upgraded to the digital and smart mode in the whole life cycle. As a critical technology for the construction industry’s innovative development, building information modeling (BIM) is widely adopted in building design, construction, and operation. BIM has gained much interest in the research field of smart buildings in recent years. However, the dimensions of BIM and smart building applications have not been explored thoroughly so far. With an in-depth review of related journal articles published from 1996 to July 2020 on the BIM applications for smart buildings, this paper provides a comprehensive understanding and critical thinking about the nexus of BIM and smart buildings. This paper proposes a framework with three dimensions for the nexus of BIM application in smart buildings, including BIM attributes, project phases, and smart attributes. According to the three dimensions, this paper elaborates on (1) the advantages of BIM for achieving various smartness; (2) applications of BIM in multiple phases of smart buildings; and (3) smart building functions that be achieved with BIM. Based on the analysis of the literature in three dimensions, this paper presents the cross-analysis of the nexus of BIM and smart buildings. Lastly, this paper proposes the critical insights and implications about the research gaps and research trends: (1) enhancing the interoperability of BIM software; (2) further exploring the role of BIM in the operation and refurbishment phase of smart buildings; (3) paying attention to BIM technology in the field of transportation infrastructure; (4) clarifying the economic benefits of BIM projects; and (5) integrating BIM and other technologies.

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“…Several recent [29,28,2,30,31,32,33,34] and less recent [35,36,37,38] reviews provide an excellent overview on the issues related to the use of MPC in buildings, including physics-based methods such as the Building Resistance-Capacitance Modeling (BRCM) toolbox [16] or Modelicabased approaches [15]. Here, we focus on the most relevant work related to our study, i.e.…”

Section: Related Workmentioning

confidence: 99%

Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC

Bünning,

Huber,

Schalbetter

et al. 2021

Preprint

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Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directly from measurement data. Machine learning methods have been widely applied to this problem and validated mostly in simulation; there are, however, few studies on a direct comparison of different models or validation in real buildings to be found in the literature. Methods that are indeed validated in application often lead to computationally complex non-convex optimization problems. Here we compare physics-informed Autoregressive-Moving-Average with Exogenous Inputs (ARMAX) models to Machine Learning models based on Random Forests and Input Convex Neural Networks and the resulting convex MPC schemes in experiments on a practical building application with the goal of minimizing energy consumption while maintaining occupant comfort, and in a numerical case study. We demonstrate that Predictive Control in general leads to savings between 26% and 49% of heating and cooling energy, compared to the building's baseline hysteresis controller. Moreover, we show that all model types lead to satisfactory control performance in terms of constraint satisfaction and energy reduction. However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models. Moreover, even if abundant training data is available, the ARMAX models have a significantly lower prediction error than the Machine Learning models, which indicates that the encoded physics-based prior of the former cannot independently be found by the latter.

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Model Predictive Control for Smart Buildings to Provide the Demand Side Flexibility in the Multi-Carrier Energy Context: Current Status, Pros and Cons, Feasibility and Barriers

Cited by 20 publications

References 2 publications

Enhancing demand-side flexibility for Heat Booster Substations in Ultra-Low Temperature District Heating systems

Enhancing demand-side flexibility for Heat Booster Substations in Ultra-Low Temperature District Heating systems

Adopting Building Information Modeling (BIM) for the Development of Smart Buildings: A Review of Enabling Applications and Challenges

Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC

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