Improved energy management of chiller systems by multivariate and data envelopment analyses

Yu, F.W.; Chan, K.T.

doi:10.1016/j.apenergy.2011.11.016

Cited by 57 publications

(22 citation statements)

References 13 publications

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“…Zucker et al [17] successfully used the X-Means algorithm to automate the detection of system states in order to examine operation data of adsorption chillers. Yu and Chan [18] also focused on chillers as a main component of energy consumption and use data envelopment to improve the energy management of chillers. Hong et al [19] focused on retrofitting of high performance buildings and use data analytics for energy profiling and benchmarking.…”

Section: State Of the Artmentioning

confidence: 99%

Sanitation and Analysis of Operation Data in Energy Systems

et al. 2015

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Abstract:We present a workflow for data sanitation and analysis of operation data with the goal of increasing energy efficiency and reliability in the operation of building-related energy systems. The workflow makes use of machine learning algorithms and innovative visualizations. The environment, in which monitoring data for energy systems are created, requires low configuration effort for data analysis. Therefore the focus lies on methods that operate automatically and require little or no configuration. As a result a generic workflow is created that is applicable to various energy-related time series data; it starts with data accessibility, followed by automated detection of duty cycles where applicable. The detection of outliers in the data and the sanitation of gaps ensure that the data quality is sufficient for an analysis by domain experts, in our case the analysis of system energy efficiency. To prove the feasibility of the approach, the sanitation and analysis workflow is implemented and applied to the recorded data of a solar driven adsorption chiller.

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Section: State Of the Artmentioning

confidence: 99%

Sanitation and Analysis of Operation Data in Energy Systems

et al. 2015

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“…DEA has recently become a popular approach for measuring the energy and environmental performance at the macro-economy level Yu and Chan, 2012;Blomberg et al, 2012).…”

Section: Selection Of Analytical Modesmentioning

confidence: 99%

The Energy Saving Strategy on the Sustainable Campus Renovation Plan by Recycling and Reuse of Rainwater in Taiwan

Liao¹,

Liu²

2017

Appl Ecol Env Res

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Liao -Liu: The energy savings strategy on the sustainable campus renovation plan by recycling and reuse of rainwater in Taiwan - Abstract. The main purpose of this study is to investigate the energy saving strategy on the Sustainable Campus Renovation Plan by recycling and reuse of rainwater in Taiwan. Case studies from the past were also investigated for effectiveness comparisons, which include both economical values and environmental values. Data Envelopment Analysis (DEA) has been implemented for quantification purposes and it provided the visualization and comparison of energy saving improvement database. Moreover, a model for assessing the improvement on the recycling and reuse of rain water could be constructed. In addition, a set of improved strategy process and steps of rain water recycling and reuse methodology may be proposed which result in the vision of the Sustainable Campus Renovation establishment.

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“…In their study [14], the number of employees, electricity, water, and LPG (Liquefied Petroleum Gas) consumption were used as inputs, and room occupancy ratio, total annual revenues, and the annual total number of customers were used as outputs. Yu and Chan [15] studied the performance of a chiller in a building. To date, there is no complete study on objective energy performance benchmarking of a whole building in relation to a large set of peer buildings.…”

Section: Introductionmentioning

confidence: 99%

Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling

Yoon

Park

2017

Sustainability

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An objective measure of building energy performance is crucial for performance assessment and rational decision making on energy retrofits and policies of existing buildings. One of the most popular measures of building energy performance benchmarking is Energy Use Intensity (EUI, kwh/m 2 ). While EUI is simple to understand, it only represents the amount of consumed energy per unit floor area rather than the real performance of a building. In other words, it cannot take into account building services such as operation hours, comfortable environment, etc. EUI is often misinterpreted by assuming that a lower EUI for a building implies better energy performance, which may not actually be the case if many of the building services are not considered. In order to overcome this limitation, this paper presents Data Envelopment Analysis (DEA) coupled with Monte Carlo sampling. DEA is a data-driven and non-parametric performance measurement method. DEA can quantify the performance of a given building given multiple inputs and multiple outputs. In this study, two existing office buildings were selected. For energy performance benchmarking, 1000 virtual peer buildings were generated from a Monte Carlo sampling and then simulated using EnergyPlus. Based on a comparison between DEA-based and EUI-based benchmarking, it is shown that DEA is more performance-oriented, objective, and rational since DEA can take into account input (energy used to provide the services used in a building) and output (level of services provided by a building). It is shown that DEA can be an objective building energy benchmarking method, and can be used to identify low energy performance buildings.

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Improved energy management of chiller systems by multivariate and data envelopment analyses

Cited by 57 publications

References 13 publications

Sanitation and Analysis of Operation Data in Energy Systems

Sanitation and Analysis of Operation Data in Energy Systems

The Energy Saving Strategy on the Sustainable Campus Renovation Plan by Recycling and Reuse of Rainwater in Taiwan

Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling

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