Measurement and classification of energy efficiency in HVAC systems

Alves, Octávio; Monteiro, Eliseu; Brito, Paulo; Romano, Pedro

doi:10.1016/j.enbuild.2016.08.070

Cited by 31 publications

(14 citation statements)

References 7 publications

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“…In addition, despite vast developments on control strategies for HVAC&R systems in the last decade (Dounis and Caraiscos 2009;Oancea and Caluianu 2013;Mirakhorli and Dong 2016), the way in which systems are controlled has not been seriously considered as a criterion for systems selection in the reviewed studies. In reality, the level of energy performance, indoor air quality, environmental impacts and occupants' well-being associated with HVAC&R systems highly depend on the strategies used to control the systems (Wu and Clements-Croome 2007b;Al horr et al 2016;Alves et al 2016;Ghaffarianhoseini et al 2016). In other words, considering the control mechanisms especially those offering practical interaction between indoor/outdoor environment, occupants and facility management team can make a huge difference on the overall performance of building and occupants' well-being.…”

Section: Discussionmentioning

confidence: 99%

Decision-making on HVAC&R systems selection: a critical review

Shahrestani

Yao

Cook

et al. 2017

Intelligent Buildings International

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Buildings account for more than 40% of total energy consumption in most countries and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand, together with the global need to impose energyefficiency measures, underlines the importance of selecting the most appropriate HVAC&R system in the early stages of a design process. However, this state-of-the-art study reveals that there is no review paper available in the open literature to critically analyse the existing methods for HVAC&R systems selection. Therefore, the aim of this paper is to critically review the body of knowledge on the adopted approach for HVAC&R systems selection. Based on the comprehensive literature review, the needs and gaps in this field are identified. It is revealed that the integration of probabilistic climate changes into the decision-making processes is one of the main areas that should be addressed in future studies. In addition, reliability and Life Cycle Cost of the systems, health and well-being, occupants' satisfaction and indoor air quality are of paramount factors that should be taken into account in the decisionmaking process for HVAC&R systems selection. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Decision-making on HVAC&R systems selection: a critical review

Shahrestani

Yao

Cook

et al. 2017

Intelligent Buildings International

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“…With regard to the measurement of energy efficiency, several indicators (EEI) can be used in the data analysis [16], ranging from the COP (Coefficient of Performance) or EER (Energy Efficiency Ratio) to more sophisticated indicators such as SCOP (Seasonal Coefficient of Performance), SEER (Seasonal Energy Efficiency Ratio) and IPLV (Integrated Part Load Value), which consider seasonal chiller operations and capacity modulation. Other research defines and uses specific EEIs [9].…”

Section: Related Workmentioning

confidence: 99%

“…If daily average outdoor temperature of the day before is lower than 12 • C, then the chiller selected to stage up/down will be an air-cooled chiller (ACCx; x = 1-5). In the range [12][13][14][15][16] • C, other rules will be taken into account. An overview of delimiting rules can be seen in Table 4.…”

Section: Actions Opposite Actionsmentioning

confidence: 99%

A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

et al. 2019

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Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

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“…Several Energy Efficiency Indicators (EEI) can be used to determine the chiller efficiency [1]. Most of the EEI require measuring the cooling power delivered to chilled water (the chiller output).…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers

Alonso

Morán

Pérez

et al. 2019

Engineering Applications of Neural Networks

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Intensive use of heating, ventilation and air conditioning (HVAC) systems in buildings entails an analysis and monitoring of their efficiency. Cooling systems are key facilities in large buildings, and particularly critical in hospitals, where chilled water production is needed as an auxiliary resource for a large number of devices. A chiller plant is often composed of several HVAC units running at the same time, being impossible to assess the individual cooling production and efficiency, since a sensor is seldom installed due to the high cost. We propose a virtual sensor that provides an estimation of the cooling production, based on a deep learning architecture that features a 2D CNN (Convolutional Neural Network) to capture relevant features on two-way matrix arrangements of chiller data involving thermodynamic variables and the refrigeration circuits of the chiller unit. Our approach has been tested on an air-cooled chiller in the chiller plant at a hospital, and compared to other state-of-the-art methods using 10-fold cross-validation. Our results report the lowest errors among the tested methods and include a comparison of the true and estimated cooling production and efficiency for a period of several days.

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Measurement and classification of energy efficiency in HVAC systems

Cited by 31 publications

References 7 publications

Decision-making on HVAC&R systems selection: a critical review

Decision-making on HVAC&R systems selection: a critical review

A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers

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