Multi-floor building heating models in MATLAB and Modelica environments

Perera, D.W.U.; Winkler, D.; Skeie, N.-O.

doi:10.1016/j.apenergy.2016.02.143

Cited by 35 publications

(14 citation statements)

References 24 publications

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“…A considerable amount of research has been carried out to develop models for use in adaptive and predictive control [10,[14][15][16][17][18]. However, most of the efforts are focused on the thermal dynamics of the modelled systems and subsystems [10].…”

Section: Introductionmentioning

confidence: 99%

Modelling indoor air carbon dioxide concentration using grey-box models

Macarulla

Casals

Carnevali

et al. 2017

Building and Environment

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Predictive control is the strategy that has the greatest reported benefits when it is implemented in a building energy management system. Predictive control requires low-order models to assess different scenarios and determine which strategy should be implemented to achieve a good compromise between comfort, energy consumption and energy cost. Usually, a deterministic approach is used to create low-order models to estimate the indoor CO2 concentration using the differential equation of the tracer-gas mass balance. However, the use of stochastic differential equations based on the tracer-gas mass balance is not common. The objective of this paper is to assess the potential of creating predictive models for a specific room using for the first time a stochastic grey-box modelling approach to estimate future CO2 concentrations. First of all, a set of stochastic differential equations are defined. Then, the model parameters are estimated using a maximum likelihood method. Different models are defined, and tested using a set of statistical methods. The approach used combines physical knowledge and information embedded in the monitored data to identify a suitable parametrization for a simple model that is more accurate than commonly used deterministic approaches. As a consequence, predictive control can be easily implemented in energy management systems.Peer ReviewedPostprint (author's final draft

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Section: Introductionmentioning

confidence: 99%

Modelling indoor air carbon dioxide concentration using grey-box models

Macarulla

Casals

Carnevali

et al. 2017

Building and Environment

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“…It is receiving growing attention in the use of modeling and simulation of building energy systems, as recent studies indicate: In (Wetter et al, 2014) a Modelica library for the simulation of building energy systems is introduced. (Ali et al, 2013), (Perera et al, 2016), (Sangi et al, 2016) and (Fuchs et al, 2016) use Modelica in order to model, simulate and investigate in building energy systems as well as district heating systems. MAS will play an important role in the control of future building energy systems (see 2.2).…”

Section: Introductionmentioning

confidence: 99%

A Platform for the Agent-based Control of HVAC Systems

Sangi¹,

Bünning²,

Fütterer³

et al. 2017

Linköping Electronic Conference Proceedings

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Attempts to develop efficient and environmentally friendly building energy systems have led to modern complex energy concepts for buildings, which have consequently initiated a need for new control strategies for them. Multiagent control, which is known with other names like agent-based control, offers a promising solution to this challenge. To the knowledge of the authors, there are 96 platforms for multi-agent systems in different programming languages available, which are mostly java-based and mainly used in logistic applications, but there is no platform in the modeling language Modelica, which is widely used for simulation of dynamic systems, especially buildings performance simulation. This lack motivated the authors to develop a platform for agent-based control of HAVC systems. The platform eliminates the dependency of models developed in Modelica on an extra interface, which is usually required to couple the models to the platforms written in any programming languages other than Modelica. This paper presents the structure of the platform and explains how the agents' communications work. The flexibility of the optimization objective is ensured through the definition of readily interchangeable cost functions. The applicability and functionality of the platform are proved by applying the platform in the control of building energy systems examples.

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“…Therefore, it is important to propose efficient tools for assisting heat system planning in urban areas. For example, [2] used MATLAB and Modelica models to assist building energy management systems to enhance efficiency in heating [3][4][5] recreated specific buildings in TRNSYS energy simulation model to evaluate the potential of retrofit strategies.…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Heat Demand with the Use of GIS-Based Tools

Wyrwa¹,

Chen²

2017

Energies

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This article presents a bottom-up approach for calculation of the useful heat demand for space heating and hot water preparation using geo-referenced datasets for buildings at the city level. This geographic information system (GIS) based approach was applied in the case study for the city of Krakow, where on the one hand the district heat network is well developed, while on the other hand there are still substantial number of buildings burning solid fuels in individual boilers and stoves, causing air pollution. The calculated heat demand was aggregated in the grid with 100 m × 100 m spatial resolution to deliver the heat map depicting the current situation for 21 buildings types. The results show that the residential buildings, in particular one-and multi-family buildings, have the highest share in overall demand for heat. By combining the results with location of the district heat (DH) network, the potential areas in its close vicinity that have sufficient heat demand density for developing the net were pointed out. Future evolution in heat demand for space heating in one-family houses was evaluated with the use of deterministic method employing building stock model. The study lays a foundation for planning the development of the heating system at the city level.

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Multi-floor building heating models in MATLAB and Modelica environments

Cited by 35 publications

References 24 publications

Modelling indoor air carbon dioxide concentration using grey-box models

Modelling indoor air carbon dioxide concentration using grey-box models

A Platform for the Agent-based Control of HVAC Systems

Mapping Urban Heat Demand with the Use of GIS-Based Tools

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