OpenBuild : An integrated simulation environment for building control

Gorecki, Tomasz T.; Qureshi, Faran A.; Jones, Colin N.

doi:10.1109/cca.2015.7320826

Cited by 32 publications

(16 citation statements)

References 15 publications

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“…where the maximization is taken row-wise. Dualizing these maximization problems [1,11] and introducing the dual variable Z associated to the inequality constraints describing P in the different maximization problems, the description of the set F reduces to (13).…”

Section: Tracking On a Finite Horizonmentioning

confidence: 99%

Guaranteeing input tracking for constrained systems: Theory and application to demand response

Gorecki

Bitlislioğlu

Stathopoulos

et al. 2015

2015 American Control Conference (ACC)

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A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. Using methods inspired from robust model predictive control, the proposed approach certifies the ability of a system to track any reference drawn from a polytopic set on a finite time horizon by solving a linear program. Optimization over a parameterization of the set of reference signals is discussed, and particular instances of parameterization of this set that result in a convex program are identified, allowing one to find the largest set of trackable signals of some class. Infinite horizon feasibility of the methods proposed is obtained through use of invariant sets, and an implicit description of such an invariant set is proposed. These results are tailored for the application of power consumption tracking for loads, where the operator of the load needs to certify in advance his ability to fulfill some requirement set by the network operator. An example of a building heating system illustrates the results.

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Section: Tracking On a Finite Horizonmentioning

confidence: 99%

Guaranteeing input tracking for constrained systems: Theory and application to demand response

Gorecki

Bitlislioğlu

Stathopoulos

et al. 2015

2015 American Control Conference (ACC)

Self Cite

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“…Controllable Loads: Small, medium and large office buildings, generated by the OpenBuild software [30]. The buildings are described as linear dynamical systems, the input to which is the thermal heat (kW ) that is entering or leaving each zone, while the output is the temperature at each zone ( • C).…”

Section: Modeling the Agentsmentioning

confidence: 99%

An Inertial Parallel and Asynchronous Forward–Backward Iteration for Distributed Convex Optimization

Stathopoulos

Jones

2019

J Optim Theory Appl

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Two characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous by construction. Introducing asynchronicity in the updates can resolve several issues that appear in the synchronous case, like load imbalances in the computations or failing communication links. However, and to the best of our knowledge, there are no instances of asynchronous versions of commonly-known algorithms combined with inertial acceleration techniques.In this work we propose an inertial asynchronous and parallel fixed-point iteration from which several new versions of existing convex optimization algorithms emanate. Departing from the norm that the frequency of the coordinates' updates should comply to some prior distribution, we propose a scheme where the only requirement is that the coordinates update within a bounded interval. We prove convergence of the sequence of iterates generated by the scheme at a linear rate. One instance of the proposed scheme is implemented to solve a distributed optimization load sharing problem in a smart grid setting and its superiority with respect to the non-accelerated version is illustrated.

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“…a) Modeling: The MPC-controlled buildings are originally modeled in EnergyPlus, obtained from [13], which are too complex to use for MPC. We use OpenBuild [14] to automatically extract a linear thermodynamic model of each building from its EnergyPlus data files. OpenBuild also outputs for each building the disturbance sequences that capture solar gains, internal gains and the effect of the ambient temperature.…”

Section: ) Buildings With Economic Model Predictive Controlmentioning

confidence: 99%

OpenBuildNet framework for distributed co-simulation of smart energy systems

Nghiem

Bitlislioğlu

Gorecki

et al. 2016

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Abstract-The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development.We introduce an open-source framework, OpenBuildNet, for distributed co-simulation of large-scale smart energy systems. Using a loose-coupling approach to co-simulate parallel processes, it can leverage and seamlessly integrate specialized simulation and computation tools in a common platform. Users can therefore benefit from the capabilities of state-of-the-art and widely used tools in each domain. OpenBuildNet is scalable and highly flexible as it uses a decentralized architecture, message-based communication, and peer-to-peer data exchange between subsystem nodes. It also provides a set of easy-to-use software tools tailored for researchers and engineers. This paper presents the architecture and tool suite of OpenBuildNet, and demonstrates its usefulness in a case study of controlling multiple buildings for demand response.

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OpenBuild : An integrated simulation environment for building control

Cited by 32 publications

References 15 publications

Guaranteeing input tracking for constrained systems: Theory and application to demand response

Guaranteeing input tracking for constrained systems: Theory and application to demand response

An Inertial Parallel and Asynchronous Forward–Backward Iteration for Distributed Convex Optimization

OpenBuildNet framework for distributed co-simulation of smart energy systems

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