Benchmarking energy performance of residential buildings using two-stage multifactor data envelopment analysis with degree-day based simple-normalization approach

Wang, Endong; Shen, Zhigang; Alp, Neslihan; Barry, Nate

doi:10.1016/j.enconman.2015.09.072

Cited by 38 publications

(23 citation statements)

References 36 publications

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“…e results, including two individual efficiency scores, the additive efficiency score, and the multiplicative efficiency score, are given in the second to the fifth columns of Table 4, respectively. We then apply model (18) to determine the solution of the proposed model (13).…”

Section: A Numerical Examplementioning

confidence: 99%

“…Only for DMU 5 , DMU 6 , the ranks of the firststage efficiency score determined by the proposed model ( 13) lie in the neighborhood of the ranks determined by Kao and Hwang's models (5) or (6). (ii) For each stage, the mean and the standard deviation of individual efficiency scores determined by the proposed model (13) are between the means and the standard deviations determined by Kao and Hwang's models ( 5) and ( 6), respectively.…”

Section: A Numerical Examplementioning

confidence: 99%

“…In the second stage for marketability, the profits and revenue are then used as inputs to generate its outputs: market value, returns, and earnings per share. Similar two-stage processes have also been studied in other applications, e.g., nonlife insurance companies by Kao and Hwang [4], nonstorable transport services by Chiou et al [5], sustainable product design by Chen et al [6], NBA teams by Yang et al [7], hotels by Huang et al [8] and Yin et al [9], banking system by Wang et al [10], Wanke and Barros [11], and Henriques et al [12], energy performance by Wang et al [13] and Zhu et al [14], supply chain management by Tajbakhsh et al [15] and Tavana et al [16], fixed cost allocation by Yu et al [17], and the sustainability of industrial sectors by Zhu et al [18]. To see the main twostage model and applications, we refer the readers to the review papers of Kao and Halkos et al [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

Chao

Cheng

2021

Mathematical Problems in Engineering

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Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.

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Section: A Numerical Examplementioning

confidence: 99%

Section: A Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

Chao

Cheng

2021

Mathematical Problems in Engineering

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“…building specifications and climate). As a result, to make the benchmarking results fair, the influences of these objective factors (referred to as noisy factors in the following) on the energy performance should be eliminated before the comparison [19,20].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques

Zhou

Lork

et al. 2019

Applied Energy

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Air conditioning (AC) accounts for a critical portion of the global energy consumption. To improve its energy performance, it is important to fairly benchmark its energy performance and provide the evaluation feedback to users. However, this task has not been well tackled in the residential sector. In this paper, we propose a data-driven approach to fairly benchmark the AC energy performance of residential rooms. First, a regression model is built for each benchmarked room so that its power consumption can be predicted given different weather conditions and AC settings. Then, all the rooms are clustered based on their areas and usual AC temperature set points. Lastly, within each cluster, rooms are benchmarked based on their predicted power consumption under uniform weather conditions and AC settings. A real-world case study was conducted with data collected from 44 residential rooms. Results show that the constructed regression models have an average prediction accuracy of 85.1% in cross-validation tests, and support vector regression with Gaussian kernel is the overall most suitable model structure for building the regression model. In the clustering step, 44 rooms are successfully clustered into seven clusters. By comparing the benchmarking scores generated by the proposed approach with two sets of scores computed from historical power consumption data, we demonstrate that the proposed approach is able to eliminate the influences of room areas, weather conditions, and AC settings on the benchmarking results. Therefore, the proposed benchmarking approach is valid and fair. As a by-product, the approach is also shown to be useful to investigate how room areas, weather conditions, and AC settings affect the AC power consumption of rooms in real life.

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“…Hui and Wan [17] used a DEA approach to examine the energy benchmarking of hotels in Hong Kong and demonstrated that DEA presented a useful benchmarking model for understanding efficiency within an organization that uses a variety of resources to provide a complex set of services in multiple locations. Wang et al [18] utilized a two-stage DEA method to benchmark the energy consumption of 189 one-story single-family buildings in Woodbine (IA, USA), combining the DEA method with Tobit regression for further efficiency analysis. Bian and Yang [19] integrated DEA and Shannon's entropy for efficiency analysis of resources and the environment.…”

Section: Introductionmentioning

confidence: 99%

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

et al. 2018

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Measurement of environmental and energy economics presents an analytical foundation for environmental decision making and policy analysis. Applications of data envelopment analysis (DEA) models in the assessment of environmental and energy economics are increasing notably. The main objective of this review paper is to provide the comprehensive overview of the application of DEA models in the fields of environmental and energy economics. In this regard, a total 145 articles published in the high-quality international journals extracted from two important databases (Web of Science and Scopus) were selected for review. The 145 selected articles are reviewed and classified based on different criteria including author(s), application scheme, different DEA models, application fields, the name of journals and year of publication. This review article provided insights into the methodological and conceptualization study in the application of DEA models in the environmental and energy economics fields. This study should enable scholars and practitioners to understand the state of art of input and output indicators of DEA in the fields of environmental and energy economics.

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Benchmarking energy performance of residential buildings using two-stage multifactor data envelopment analysis with degree-day based simple-normalization approach

Cited by 38 publications

References 36 publications

Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

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