HVAC control methods - a review

Belić, Filip; Hocenski, Željko; Slišković, Dražen

doi:10.1109/icstcc.2015.7321372

Cited by 42 publications

(26 citation statements)

References 38 publications

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“…The control horizon is at most as long as the prediction horizon, which usually corresponds to half of the prediction horizon based on the calculation duration. At the end of the control horizon, the last control signal is kept constant until the end of the prediction horizon [24,25]. Individually defined limitations of output and control variables are included in the optimisation problem via slack variables.…”

Section: Methodology and Fundamentals Of The Used Model Predictive Comentioning

confidence: 99%

“…Publications dealing exclusively with the comparison of control methods for air conditioning systems and providing a comprehensive overview of the state of research favour the use of MPC to solve the challenges of flexible operation mentioned above [19,24]. In both studies, the authors summarise that classical regulatory approaches are primarily suitable for less complex regulatory tasks.…”

Section: State Of Researchmentioning

confidence: 99%

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Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

et al. 2018

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The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

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Section: Methodology and Fundamentals Of The Used Model Predictive Comentioning

confidence: 99%

Section: State Of Researchmentioning

confidence: 99%

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

et al. 2018

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“…Grey-box models are quite popular in behavior prediction and control of a building or a group of buildings. Belic et al analyzed a two storey family house by representing each wall inside the house with a different RC network in [13]. Ji et al investigated the use of a lumped parameter model to estimate the hourly cooling load of a shopping mall in Shanghai [14].…”

Section: Grey-box Modellingmentioning

confidence: 99%

A generalization approach for reduced order modelling of commercial buildings

Shamsi

Ali

O’Donnell

2019

Journal of Building Performance Simulation

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Grey-box techniques can counter the computational inefficiency and resource-intensive nature of the conventional complex whitebox models. However, these approaches might tend to be too specific in their application and scalability is limited by network order. To overcome these challenges, this study proposes a generalized approach for selection of reduced-order RC network models for commercial buildings using the peak power consumption characterization. The devised methodology is used to design the RC networks of buildings connected to district heating network at University College Dublin. The close proximity between measured and simulated demand indicate the influence of power demand on RC network selection.

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“…For a more detailed review on building control we refer to the surveys concerning HVAC control based on ANN [21], time-series [22], and other soft-computing techniques [23].…”

Section: Related Workmentioning

confidence: 99%

Data-driven Optimization of Energy Efficiency and Comfort in an Apartment

Avendano

Ruyssinck

Vandekerckhove

et al. 2018

2018 International Conference on Intelligent Systems (IS)

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An important challenge in home automation is the energy efficient optimization of the indoor environment. This relies on the solution of a multi-objective optimization problem where energy efficiency and comfort parameters are maximized simultaneously. This paper presents three data-driven control algorithms based on machine learning techniques, which offer an alternative to traditional control methods. The results demonstrate that some data-driven methods can achieve similar results than rule-based systems. Moreover, they require no prior expert knowledge and have better scalability than standard approaches.

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HVAC control methods - a review

Cited by 42 publications

References 38 publications

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

A generalization approach for reduced order modelling of commercial buildings

Data-driven Optimization of Energy Efficiency and Comfort in an Apartment

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