Development of a simulation platform based on dynamic models for HVAC control analysis

Chen, Yan; Treado, Stephen J.

doi:10.1016/j.enbuild.2013.09.016

Cited by 53 publications

(19 citation statements)

References 10 publications

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“…For building applications, SIMULINK is primarily used for modeling HVAC systems and controls [17][18][19][20][21][22]. Other research groups have developed comprehensive libraries for modeling energy and hygrothermal performance of full scale buildings.…”

Section: Matlab/simulink and Application For Modeling Building Performentioning

confidence: 99%

Numerical modeling of thermal behaviors of active multi-layer living wall

Mankibi

Zhai

Al‐Saadi

et al. 2015

Energy and Buildings

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Section: Matlab/simulink and Application For Modeling Building Performentioning

confidence: 99%

Numerical modeling of thermal behaviors of active multi-layer living wall

Mankibi

Zhai

Al‐Saadi

et al. 2015

Energy and Buildings

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“…Fig. 2 describes the schematic of the single-zone HVAC system which can be found in Chen [8].However, unlike Chen' model which is built using Simulink customized blocks, we build this model using Modelica Buildings library in Dymola which was introduced in section 2. In this example, we first build a single-zone HVAC system using Modelica Buildings library including boundary, heat exchangers, actuators, sensors, movers and rooms which are described in Fig.3.…”

Section: Examplementioning

confidence: 99%

Building energy and control system modeling and simulation using Modelica

Xiong

Shi

et al. 2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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To design and operate energy efficient buildings, it is important to properly account for the dynamic system performance over a wide range of time scales and operating conditions. At the time scale of hours to days, the system dynamics determine how much energy can be stored passively or actively to exploit natural sources for heating and cooling. At the time scale of minutes or even seconds, the system dynamics determine whether equipment is cycling, which can negatively impact its energy performance and may cause premature equipment failure. To design control algorithms that exploit such behavior in order to increase system-level efficient, simulation models need to reflect the change in efficient at various steady-state and dynamic operating conditions[1]. As buildings become increasingly integrated to reduce energy and peak power and to increase occupant comfort and productivity, new challenges are posed to building simulation programs to support decision making during product development, building design, commissioning and operation [2]. These applications require the integration of multiple domains (air-flow, thermodynamics, controls, indoor environmental quality, and electrical grid) and multiple disciplines (HVAC/energy consultant, architect, controls engineer, electrical engineer).Using building simulation programs for such applications leads to new requirements for modeling and simulation tools. A lot of building simulation programs has been developed for the past decades, such as Energy Plus, ESP-r, SPARK, IDAICE or TRNSYS which are written using an imperative language (FORTRAN, C or C++).However, today's building simulation programs were primarily designed as tools for energy analysis and use of these

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“…Chantrellea [17] developed a multi-criteria tool called MultiOpt for optimizing the building renovation operations, including building envelopes, heating and cooling loads and control strategies. For HVAC operations, a dynamic optimization method was applied to analyze the control strategies of an HVAC modeling system based on the Simulink library [20]. Among the reported optimization tools, GenOpt is the most used tool in the literature set.…”

Section: Different Optimization Frameworkmentioning

confidence: 99%

Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

Pan

Xue

et al. 2017

Energies

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Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

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Development of a simulation platform based on dynamic models for HVAC control analysis

Cited by 53 publications

References 10 publications

Numerical modeling of thermal behaviors of active multi-layer living wall

Numerical modeling of thermal behaviors of active multi-layer living wall

Building energy and control system modeling and simulation using Modelica

Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

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