Controllability of buildings: A multi-input multi-output stability assessment method for buildings with slow acting heating systems

Counsell, John; Khalid, Yousaf Ali; Brindley, J.

doi:10.1016/j.simpat.2010.08.006

Cited by 14 publications

(16 citation statements)

References 15 publications

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“…-managerial issues of ensuring the stability of object indicators throughout the life cycle [1][2][3]; -tasks of increasing the effectiveness of expert systems through the integration of expert assessment in the dynamic structures of instrumental and information monitoring of buildings [4][5][6][7]; -methodological aspects of the functioning of information systems for diagnosing and forecasting technical condition through information management [8][9][10].…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

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Development of the technique of expert assessment in the diagnosis of the technical condition of buildings

Grigorovskiy

Terentyev

Mikautadze

2017

TAPR

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Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

“…In the opinion of the authors of [6], in order to assess the building parameters, it is possible to use methods for designing control systems for aircraft. The proposed model is used to more effectively manage technical parameters that ensure reliable operation of the building.…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

Development of the technique of expert assessment in the diagnosis of the technical condition of buildings

Grigorovskiy

Terentyev

Mikautadze

2017

TAPR

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“…Currently, efforts have been made to develop approaches and methods for assessing buildings [11], methods to integrate daylight and thermal efficiency [12][13][14], photovoltaic systems [15] and systems of multiobjective optimization for urban design [16] that take various aspects into account when we create an energy optimization system. Some systems address parts of the optimization problem posed above, such as GenOpt [17], an optimization program for minimizing a cost function that is evaluated by an external simulation program, or OptEPLus [18], an application focused on the tertiary sector.…”

Section: State Of the Artmentioning

confidence: 99%

Formal simulation model to optimize building sustainability

Casas

Soriano

et al. 2014

Advances in Engineering Software

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. b s t r a c tIn this work, we present a simulation model that makes it possible to find optimal values for various building parameters and the associated impacts that reduce the energy demand or consumption of the building. In the study, we consider several situations with different levels of thermal insulation. To define and to integrate the different models, a formal language (Specification and Description Language, SDL) is used. The main reason for using this formal language is that it makes it possible to define simulation models from graphical diagrams in an unambiguous and standard way. This simplifies the multidisciplinary interaction between team members. Additionally, the fact that SDL is an ISO standard simplifies its implementation because several tools understand this language. This simplification of the model makes it possible to increase the model credibility and simplify the validation and verification processes. In the present project, the simulation tools used were SDLPS (to rule the main simulation process) and Energy+ (as a calculus engine for energy demand). The interactions between all these tools are detailed and specified in the model, allowing a deeper comprehension of the process that define the life of a building from the point of view of its sustainability.

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“…The building model used for controller analysis in this research is based on the dynamic model developed in Murphy et al (2011) and Counsell et al (2011). The zone model consists of four state variables for temperature and one state variable for humidity.…”

Section: Zone Model and Hvac System Descriptionmentioning

confidence: 99%

Robust nonlinear HVAC systems control with evolutionary optimisation

Counsell

Zaher

Brindley

et al. 2013

Engineering Computations

Self Cite

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Purpose -The purpose of this research is to design a robust high-performance nonlinear multi-input multi-output heating, ventilation and air conditioning (HVAC) system controller for temperature and relative humidity regulation. Buildings are complex systems which are subjected to many unknown disturbances. Further complicating the control problem is the fact that, in practice, buildings and their systems have static nonlinearities such as power saturation that make stability difficult to guarantee. Therefore, in order to overcome these issues, a control system must be designed to be robust (performance insensitive) against uncertainties, static nonlinearities and effectively respond to unknown heat load and moisture disturbances. Design/methodology/approach -A state of the art nonlinear inverse dynamics (NID) technique is combined with a genetic algorithm (GA) optimisation scheme in order to improve robustness against uncertainty in the system's modelling assumptions. The parameter uncertainty problem is addressed by optimising the control system parameters over a specified range of uncertainty. The NID control structure provides further robustness with effective disturbance handling and a stability criteria that holds in the presence of actuator saturation. Findings -The proposed method delivers significantly more energy efficient performance whilst achieving improved thermal comfort when compared with a current industry standard HVAC controller design such as proportional-integral-derivative. The expected excellent response to disturbances is also demonstrated. Research limitations/implications -This method can easily be extended to account for other parameters with a specified uncertainty range. Practical implications -This research presents a method of optimised NID controller design which can be easily implemented in real HVAC controllers of building energy management systems with a high degree of confidence to provide high levels of thermal comfort whilst significantly reducing energy usage. Originality/value -A novel HVAC optimised NID control strategy using the robust inverse dynamics estimation feedback control topology with GA optimisation for improved robustness and tuning over a range of parameter uncertainty is described, designed and its performance benefits shown through simulation studies.

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Controllability of buildings: A multi-input multi-output stability assessment method for buildings with slow acting heating systems

Cited by 14 publications

References 15 publications

Development of the technique of expert assessment in the diagnosis of the technical condition of buildings

Development of the technique of expert assessment in the diagnosis of the technical condition of buildings

Formal simulation model to optimize building sustainability

Robust nonlinear HVAC systems control with evolutionary optimisation

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