Developing a simplified model for evaluating chiller-system configurations

Lee, Wai Ling; Lee, S. H.

doi:10.1016/j.apenergy.2006.07.006

Cited by 23 publications

(2 citation statements)

References 13 publications

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“…How can we provide a model of an energy system for monitoring purposes? Several models are proposed, many equations are already edited in handbooks, among them Ashrae's transactions for thermal modeling [4,7] and in other papers as [8][9][10].…”

Section: Model Derivation Strategiesmentioning

confidence: 99%

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

Lai¹,

Magoulès²,

Lherminier³

2008

International Journal of Computer Mathematics

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For the purpose of energy conservation, we present in this paper an introduction to the use of Support Vector (SV) Learning Machines used as a data mining tool applied to buildings energy consumption data from a measurement campaign. Experiments using a SVM-based software tool for the prediction of the electrical consumption of a residential building is performed. The data included one year and three months of daily recordings of electrical consumption and climate data such as temperatures and humidities. The learning stage was done for a first part of the data, the predictions were done for the last month. Performances of the model and contributions of significant factors were also derived. The results show good performances for the model. Besides the second experiment consists in model re-estimations on a one-year daily recording dataset lagged at one-day time intervals in such a way that we derive temporal series of influencing factors weights along with model performance criteria. Finally we introduce a perturbation in one of the influencing variable to detect a model change. Comparing contributing weights with and without the perturbation, the sudden contributing weight change could have diagnosed the perturbation. The important point is the ease of the production of many models. This method announces future research work in the exploitation of possibilities of this "model factory".

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Section: Model Derivation Strategiesmentioning

confidence: 99%

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

Lai¹,

Magoulès²,

Lherminier³

2008

International Journal of Computer Mathematics

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“…Huang et al [14] found that the life cycle cost of a plant can be reduced by up to 12.5% as the design parameters, i.e., the number of chillers and the distribution of the cooling capacity between them, are optimized. Furthermore, Lee and Lee [26] developed a simplified statistics-based method to preliminary assess the configuration of multiplechillers systems. The authors observed an energy saving of up to 9.5% by changing the number of chillers.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost

Torres¹,

Gullo

Herrera

et al. 2022

Sustainability

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An appropriate design of a chiller plant is crucial to guarantee highly performing solutions. However, several design variables, such as type of systems, total cooling capacity, and hydraulic arrangement, need to be considered. On the one hand, at present, different technical criteria for selecting the most suitable design variables are available. Studies that corroborate the influence of the design variables over the operational variables are missing. In order to fill this knowledge gap, this work proposes a statistical analysis of design variables in chiller plants operating in medium- and large-scale applications and evaluates their influence on energy consumption and life cycle cost (LCC) under the same thermal demand conditions. A case study involving 138 chiller plant combinations featuring different arrangements and a Cuban hotel was selected. The results suggested that the total chiller design and cooling capacity distribution among chillers have a significant influence on the energy consumption of the chiller plant with a Spearman’s Rho and Kendall Tau (τ) correlation index value of −0.625 and 0.559, respectively. However, with LCC, only the cooling capacity distribution among the chillers had a certain influence with a Kendall Tau correlation index value of 0.289. As for the considered total cooling capacity, the applied statistical test showed that this design variable does not have any influence on performing the chiller plant.

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Optimization of Multiple Chiller Systems Using TLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

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Developing a simplified model for evaluating chiller-system configurations

Cited by 23 publications

References 13 publications

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost

Optimization of Multiple Chiller Systems Using TLBO Algorithm

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