Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization

Zeng, Tingting; Brooks, Jonathan; Barooah, Prabir

doi:10.1016/j.automatica.2021.109631

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…In our case, it will be a black-box method and thus the construction of the model and the identification of parameters will depend only on the measured input and output data from the process. Such an approach to identification is often used in the field of building automation (see e.g., [17][18][19][20][21][22]). The implementation of online system identification stems not only from the need for advanced control techniques (e.g., in [23]) and other reasons discussed above, but also from the experience of employees of the Prosystemy company who work as operators and building managers.…”

Section: Hvac Processes Identificationmentioning

confidence: 99%

A Data-Driven Identification Procedure for HVAC Processes with Laboratory and Real-World Validation

Minarčík

Procházka²,

Gulan

2021

Processes

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Linear system identification is a well-known methodology for building mathematical models of dynamic systems from observed input–output data. It also represents an essential tool for model-based control design, adaptive control and other advanced control techniques. Use of linear identification is, however, often limited to academic environment and to research facilities equipped with scientific computing platforms and highly qualified staff. Common industrial or building control system technology rarely uses these advanced design techniques. The main obstacle is typically lack of experience with their practical implementation. In this article, a procedure is proposed, implemented, and tested, that brings the benefits of linear identification into broader control system practice. The open-source DCU control system platform with its advanced control framework is used for implementation of the proposed linear identification procedure. The procedure is experimentally tested in the laboratory setting using a unique model of HVAC system as well as in real-world environment in an experimental two storey family house. Testing this novel feature of the control system has proved satisfactory results, while some of them are presented in graphical and numerical form.

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Section: Hvac Processes Identificationmentioning

confidence: 99%

A Data-Driven Identification Procedure for HVAC Processes with Laboratory and Real-World Validation

Minarčík

Procházka²,

Gulan

2021

Processes

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“…The method is not tested in closed-loop MPC. In [57], a method for simultaneous plant and disturbance identification to for buildings is presented. The authors use the Lasso method to promote sparsity and constrain coefficients to obtain linear time-invariant input-output stable models with positive DC gains.…”

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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“…Identifying a linear black box model directly from data is also not straightforward (we discuss this later in Section 3). Recent progress in this direction is made in [21,16], which identifies a linear model in which the input is the heat gain due to the HVAC system. However, the model is still not linear with respect to the control commands such as air flow rate.…”

Section: Literature Reviewmentioning

confidence: 99%

An autonomous MPC scheme for energy-efficient control of building HVAC systems

Zeng

Barooah

2020

2020 American Control Conference (ACC)

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An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture-periodic model and disturbance update and convexification of the planning problem-are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.

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Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization

Cited by 9 publications

References 9 publications

A Data-Driven Identification Procedure for HVAC Processes with Laboratory and Real-World Validation

A Data-Driven Identification Procedure for HVAC Processes with Laboratory and Real-World Validation

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

An autonomous MPC scheme for energy-efficient control of building HVAC systems

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