Energy analysis of chilled water system configurations using simulation-based optimization

Ali, Muzaffar; Vuković, Vladimir; Sahir, Mukhtar Hussain; Fontanella, Giuliano

doi:10.1016/j.enbuild.2012.12.011

Cited by 38 publications

(14 citation statements)

References 14 publications

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“…1) Reduce population size or the generation number of GA (Ali et al 2013); 2) Apply simplified modeling or alternate models instead of complex and detailed modeling (Magnier, Haghighat 2010;Peippo et al 1999). In the issue mentioned in this paper, genetic algorithms optimized operations by changing the shape, arrangement, and form of buildings and measuring the required energy consumption per kilo watts per square meter.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Due to the changes in domain of continuous operations, randomized optimization algorithms including GA (Genetic Algorithm) (Mossolly et al 2009;Magnier, Haghighat 2010;Wright et al 2002;Wang et al 2005), PSO (Particle Swarm Optimization) (Ali et al 2013;Djuric et al 2007), and generative systems are often used to solve optimization problems, like building simulation models. However, achieving the optimal solution often requires hundreds or thousands of simulations using randomized algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Computational Optimization of Housing Complexes Forms to Enhance Energy Efficiency

Derazgisou

Baušys

Fayaz

2018

Journal of Civil Engineering and Management

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Abstract. This study aimed to consider the field of energy saving in architectural design utilizing computer analysis and calculation. In this analysis, architecture design with an approach to optimizing energy consumption in the design of individual units, complex plan sites, and apartment sets using a computer was studied. Parameters affecting this research include the geometry of units, the arrangement and location relationship of buildings, and the form and height of apartment units. Different plans were produced by utilizing the initial plan of the designer and changing some aspects of it approved by the architectural design using the parametric modeling technique. Utilizing similar logic and a shift in the arrangement of buildings on the site, a variety of options were produced. By selecting existing and pre-designed plans, the optimal form was produced by computer. After computer-simulating each option, the energy analysis process was started for each building design. In the optimization process for each of the three designs, a genetic algorithm was used to achieve the optimal solution. After accomplishing the various stages of optimization, the final option compared with the initial design had reductions in energy consumption of 21% in plan design, 2% in site plan design, and 26% in apartment units form design. It should be noted that the processes of simulation and optimization were performed in the context of a continuous algorithm and by utilizing parametric tools that reduced the duration of this process.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Optimization of Housing Complexes Forms to Enhance Energy Efficiency

Derazgisou

Baušys

Fayaz

2018

Journal of Civil Engineering and Management

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“…The execution of a Modelica model also performs automatic differentiation followed by the generation and compilation of a C/C++ code (Ali et al (2013)). …”

Section: The Modelica Languagementioning

confidence: 99%

“…to provide a simulation tool for testing advanced control solutions to be integrated in chilled water systems (Ali et al (2013)). The Modelica/Dymola simulation tool can manage to simulate highly integrated and complex energy and control systems, even because a number of libraries are available within the Modelica package, that can be extended beyond the use envisaged by developers (Wetter (2011)).…”

Section: The Modelica Languagementioning

confidence: 99%

Development and calibration of a model for the dynamic simulation of fans with induction motors

Vaccarini

Carbonari

Casals

2017

Applied Thermal Engineering

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In this paper a model for the dynamic simulation of fans used in mechanical air supply systems is described. Thanks to this model, the behavior of fans subject to control by variable frequency drives (VFD) can be predicted, which includes power absorbed by the fan and expected ventilation rates. Hence, it can help design energy control systems for buildings. The proposed model was based on the Modelica language and was developed from the dynamic phasor domain representation, because this representation is a trade-off between the basic non transient representation, that is computationally efficient but cannot describe fan dynamics, and the dynamic time domain model, that is the most representative one but computationally very demanding. A comparison among these models showed that, within fan frequency variations typical of ventilation systems in buildings, the phasor domain model is as representative as the more complex dynamic time domain model in terms of prediction of the dynamic behavior, that is neglected by the basic non transient model. Moreover, the new phasor domain model was validated against measured data relative to a fan installed in a subway station in Barcelona. Thanks to this model, energy consumption of dynamically driven fans can be estimated at the simulation stage, at the expense of a reasonable computational effort.Peer ReviewedPostprint (author's final draft

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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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Energy analysis of chilled water system configurations using simulation-based optimization

Cited by 38 publications

References 14 publications

Computational Optimization of Housing Complexes Forms to Enhance Energy Efficiency

Computational Optimization of Housing Complexes Forms to Enhance Energy Efficiency

Development and calibration of a model for the dynamic simulation of fans with induction motors

A Platform for the Agent-based Control of HVAC Systems

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