Action-oriented Benchmarking: Using the CEUS Database to Benchmark Commercial Buildings in California

Mathew, Paul; Mills, Evan; Bourassa, Norman; Brook, Martha

doi:10.1080/01998590809509386

Cited by 17 publications

(15 citation statements)

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“…Building energy benchmarking is a mechanism to evaluate and compare the energy performance of a building, relative to other similar buildings or a reference building in order to give stakeholders information and motivate energy retrofits [1][2][3]. The energy benchmark can be defined as a representative value of the energy performance of a peer group with similar properties to a given building.…”

Section: Introductionmentioning

confidence: 99%

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Kim

2020

Energies

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In an effort to improve the energy efficiency of existing buildings, it is necessary to first evaluate the energy performance of those buildings. Since it is difficult to obtain detailed information on existing buildings, the challenge is how to conduct reliable energy performance assessments with this limited information. As a result, many countries have adopted evaluation systems based on measured energy consumption data for existing buildings. This study aims to analyze the building energy consumption and characteristics using Korea’s national building database and provide an energy performance benchmark for continuous management of the energy performance of existing buildings. We analyzed the relationship between the basic statistical characteristics of the information collected from the national integrated energy database and energy consumption. The total floor area was found to be closely related to energy consumption, and various regression analysis methods were applied and compared to develop a benchmark to explain the trends of energy consumption according to the increase in total floor area. Finally, the developed benchmarks were used to evaluate energy consumption and examine the feasibility of the benchmarks.

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Section: Introductionmentioning

confidence: 99%

Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database

Kim

2020

Energies

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“…Calibration is a manual or automatic procedure that aligns the model with the user input data and enables an improved representation of the actual building [60]. Although retrofit tools use calibrated energy models for their retrofit analysis, their underlying calibration techniques are unclear.…”

Section: Discussionmentioning

confidence: 99%

Energy retrofit analysis toolkits for commercial buildings: A review

Lee

Hong

Piette

et al. 2015

Energy

102

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a b s t r a c tRetrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-todate review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.

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“…We suggest a hierarchical classification scheme that allows classification at different levels of granularity based on data availability i.e. while it is generally preferable to benchmark with a classification that is as fine-grained as possible, data limitations may require a more coarse classification [Mathew et al 2008].…”

Section: Special Considerationsmentioning

confidence: 99%