PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants

Donaisky, Emerson; Oliveira, Gustavo H. C.; Freire, Roberto Zanetti; Mendes, Nathan

doi:10.1109/cca.2007.4389227

Cited by 28 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These must be obtained using previous predictions. This way, the computation of the predictions over a prediction horizon PH may require PH executions of model (6).…”

Section: Predictive Models Designmentioning

confidence: 99%

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Ruano

Pesteh

Silva

et al. 2016

Energy and Buildings

View full text Add to dashboard Cite

“…These must be obtained using previous predictions. This way, the computation of the predictions over a prediction horizon PH may require PH executions of model (6).…”

Section: Predictive Models Designmentioning

confidence: 99%

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Ruano

Pesteh

Silva

et al. 2016

Energy and Buildings

View full text Add to dashboard Cite

“…O presente artigo expande os trabalhos de Freire et al (2005b), Freire et al (2008), Donaisky et al (2007), Donaisky et al (2008) e Donaisky (2008 propondo dois algoritmos MBPC para o controle de conforto térmico baseado em PMV (PMV-MBPC). Os algoritmos são caracterizados pelos seguintes pontos: i) a entrada do processoé representada por um simples equipamento de climatização (um sinal de controle) e dois sensores internos para medir a temperatura e a umidade relativa; ii) oíndice PMVé considerado para promover conforto térmico aos ocupantes.…”

Section: Leis De Controle Preditivo Para Conforto Térmicounclassified

“…Alguns exemplos são os trabalhos de Kolokotsa et al (2001) e Gouda et al (2001), no contexto de algoritmos de controle PID (Proporcional, Integral e Derivativo) e Fuzzy. Por outro lado, oíndice PMV pode ser incluído na função custo para gerar uma lei de controle de conforto térmico baseado nos fundamentos do MBPC (Model Based Predictive Control ou Controle Preditivo Baseado em Modelo) (Freire et al, 2005a;Freire et al, 2005b;Freire et al, 2008;Donaisky et al, 2007;Donaisky et al, 2008;Donaisky, 2008).…”

Section: Introductionunclassified

Algoritmos PMV-MBPC para conforto térmico em edificações e aplicação em uma célula-teste

Donaisky

Oliveira

Mendes

2010

Sba Controle & Automação

Self Cite

View full text Add to dashboard Cite

PMV-MBPC Algorithms for Thermal Comfort: An Application to a Test-cellThe present paper is focused on thermal comfort control problem for building occupants. Thermal comfort is a concept difficult to define and, here, the PMV (Predicted Mean Vote) index is used by means of two predictive control strategies, characterized by having terminal constraints, called here PMV-MBPC (PMV Model Based Predictive Controller). The first thermal comfort control strategy is based on generating a temperature set-point signal that optimizes the building (single zone) internal PMV value. The second one includes the PMV model in the controller prediction computations, generating a non-linear PMV model having Wiener structure. Results related to closed loop system stability are proposed. In this context, an environment for control systems tests is described and the first approach is then implemented in real time using an oil-heater and this environment. Experimental results illustrate the thermal comfort control system performance. Additionally, simulation

show abstract

“…• The majority are data-driven, relying on some sort of statistical regression [1], [5], [6], [7], [11], [12], [13], [15], [24], [27], and therefore are not generic enough. • Many BEMS methods focus on the optimization and control of individual subsystems, without considering interplay of the various systems.…”

Section: Introductionmentioning

confidence: 99%

A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings

Giannakis

Kontes

Kosmatopoulos

et al. 2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

View full text Add to dashboard Cite

Building Energy Management Systems are finding widespread use for the holistic control of all energy-influencing elements of buildings and are responsible for ensuring an effective and parsimonious energy use. In most cases, fixedlogic controllers are deployed in the building to implement predetermined strategies. Good performance can not be guaranteed due to inherent uncertainties that can not be a priori ascertained, such as weather variations, occupant actions, and changes in the building state and characteristics. In this paper, a model-assisted tuning methodology is presented to adaptively and automatically fine-tune relevant controller parameters. In our approach, at the end of each day of the building operation, given "reasonable" predictions for the following day, and using an accurate thermal-simulation model to evaluate performance, a new set of controller parameters is generated to be used the following day. This way, good performance can be achieved using controllers with simple mathematical structure.

show abstract

PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants

Cited by 28 publications

References 5 publications

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

Algoritmos PMV-MBPC para conforto térmico em edificações e aplicação em uma célula-teste

A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings

Contact Info

Product

Resources

About