Energy optimization methodology of multi-chiller plant in commercial buildings

Thangavelu, Sundar Raj; Myat, Aung; Khambadkone, A.M.

doi:10.1016/j.energy.2017.01.116

Cited by 116 publications

(42 citation statements)

References 30 publications

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“…Most PMSs rely on complex optimization methods [31][32][33], which require a high computational effort and make the deployment on existing controllers so difficult that often the rules of these PMSs can be only tested on simulated plants. Other commercial software uses relational control, based on the equal marginal performance principle [34].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2019

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“…There are many attempts to reduce energy consumption for the chiller systems including by using multiple linear regression analysis, interaction analysis of each components, and data-driven analysis [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

2019

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This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

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“…For a 24 h time horizon, the energy savings are around 9.4% and the energy costs savings are 17.4%. [16] A literature review reveals that the energy-saving potential varies between 5% [17] and 40% [18] depending on the case study.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Cooling Utility System with Continuous Self-Learning Performance Models

et al. 2019

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Prerequisite for an efficient cooling energy system is the knowledge and optimal combination of different operating conditions of individual compression and free cooling chillers. The performance of cooling systems depends on their part-load performance and their condensing temperature, which are often not continuously measured. Recorded energy data remain unused, and manufacturers’ data differ from the real performance. For this purpose, manufacturer and real data are combined and continuously adapted to form part-load chiller models. This study applied a predictive optimization algorithm to calculate the optimal operating conditions of multiple chillers. A sprinkler tank offers the opportunity to store cold-water for later utilization. This potential is used to show the load shifting potential of the cooling system by using a variable electricity price as an input variable to the optimization. The set points from the optimization have been continuously adjusted throughout a dynamic simulation. A case study of a plastic processing company evaluates different scenarios against the status quo. Applying an optimal chiller sequencing and charging strategy of a sprinkler tank leads to electrical energy savings of up to 43%. Purchasing electricity on the EPEX SPOT market leads to additional costs savings of up to 17%. The total energy savings highly depend on the weather conditions and the prediction horizon.

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Energy optimization methodology of multi-chiller plant in commercial buildings

Cited by 116 publications

References 30 publications

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

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

Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Optimization of Cooling Utility System with Continuous Self-Learning Performance Models

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