A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Atam, Ercan; Helsen, Lieve

doi:10.1016/j.enbuild.2015.02.026

Cited by 29 publications

(15 citation statements)

References 18 publications

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“…When considering building climate control, MPC has received considerable attention in the recent literature [6,7,8,26]. The overview of practical issues related to the implementation of an MPC controller can be found in [27].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Experimental analysis of data-driven control for a building heating system

Costanzo

Iacovella

Ruelens

et al. 2016

Sustainable Energy, Grids and Networks

101

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Driven by the opportunity to harvest the flexibility related to building climate control for demand response applications, this work presents a data-driven control approach building upon recent advancements in reinforcement learning. More specifically, modelassisted batch reinforcement learning is applied to the setting of building climate control subjected to a dynamic pricing. The underlying sequential decision making problem is cast on a Markov decision problem, after which the control algorithm is detailed. In this work, fitted Q-iteration is used to construct a policy from a batch of experimental tuples. In those regions of the state space where the experimental sample density is low, virtual support tuples are added using an artificial neural network. Finally, the resulting policy is shaped using domain knowledge. The control approach has been evaluated quantitatively using a simulation and qualitatively in a living lab. From the quantitative analysis it has been found that the control approach converges in approximately 20 days to obtain a control policy with a performance within 90% of the mathematical optimum. The experimental analysis confirms that within 10 to 20 days sensible policies are obtained that can be used for different outside temperature regimes.

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Section: Model Predictive Controlmentioning

confidence: 99%

Experimental analysis of data-driven control for a building heating system

Costanzo

Iacovella

Ruelens

et al. 2016

Sustainable Energy, Grids and Networks

101

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“…G LOBAL warming and worries about reliable future energy supply have arisen a substantial interest in clean and renewable energy. Especially, last two decades have witnessed a substantial increase in geothermal energy use through GCHPs [1]- [8] and GCHP systems with supplementary devices (HyGCHPs) [9]- [16]. Based on the fact that buildings account for approximately 40% of total energy use in Europe [18] and similar percentages in the rest of the world, development of energy-efficient buildings and heating and cooling technologies together with appropriate control strategies is required.…”

Section: Nomenclature C Ementioning

confidence: 99%

“…Next, we will express the borehole dynamics (10) in terms of the u and w. Using (4) and (16), the net input to the ground can be expressed as…”

Section: A Dynamic Programming-based Optimal Controlmentioning

confidence: 99%

Optimal Control Approaches for Analysis of Energy Use Minimization of Hybrid Ground-Coupled Heat Pump Systems

Atam

Patteeuw

Antonov

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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In this paper, we present a prediction-based dynamic programming (DP) control approach, a nonlinear model predictive control (NMPC) approach, and a linear optimal control (LOC) approach to analyze the minimization of the total energy use of a hybrid ground-coupled heat pump (hp) system (incorporating a ground-coupled hp, a gas boiler, a passive cooler, and an active chiller) under operational constraints. A large-scale emulator model (based on finite-volume method and the equivalent-diameter approach) is used for the borehole system and for the assessment of different control algorithms. A nonlinear autoregressive exogenous model is identified from the inputoutput data generated by the emulator model to be used in a DP-based controller. Since DP is a global optimal control method, it was used as a reference for performance assessment. Next, a state-space reduced-order control-oriented model with a larger sampling time is obtained from the emulator model using the so-called proper orthogonal decomposition model reduction technique. This model is used in an NMPC algorithm to see how much NMPC is suboptimal with respect to the DP in terms of annual energy use minimization. Finally, a series of LOCs based on constant hp coefficients of performance is tested to see how much the system performance deteriorates. The control algorithms are used for the satisfaction of heating-cooling demands of three types of buildings: 1) heating dominated; 2) cooling dominated; and 3) thermally balanced. The effects of constraining thermal buildup/depletion of ground, variable electricity prices, and marginal violation of thermal comfort on the performance of the different controllers applied are also separately analyzed.

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“…Switching between controllers can cause poor performance. The MPC planning problem is usually non-convex due to bilinearities in models and cost functions [6,8,13,14,15]. Most works on HVAC MPC ignore the issue of reliability of the decisions computed by a non-convex planner, especially over long periods of operation.…”

Section: Introductionmentioning

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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A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Cited by 29 publications

References 18 publications

Experimental analysis of data-driven control for a building heating system

Experimental analysis of data-driven control for a building heating system

Optimal Control Approaches for Analysis of Energy Use Minimization of Hybrid Ground-Coupled Heat Pump Systems

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

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