Methods for benchmarking building energy consumption against its past or intended performance: An overview

Li, Zhengwei; Han, Yue; Xu, Peng

doi:10.1016/j.apenergy.2014.03.020

Cited by 168 publications

(54 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several survey papers have discussed the problem of benchmarking building energy consumption (Chung 2011;Li et al 2014;Pérez-Lombard et al 2009) and reviewed various concepts involved in assessing building energy efficiency, including benchmarking, energy rating, and energy labeling. They also discussed the development of an energy certification scheme and highlighted main considerations such as what and how it should be calculated.…”

Section: Related Workmentioning

confidence: 99%

“…The author also presented a literature review of how these methods and their variants have been used for building energy benchmarking. Li et al (2014) reviewed methods for benchmarking building energy consumption against its past or intended performance. They classified the methods presented into three categories: white, gray, and black box methods.…”

Section: Related Workmentioning

confidence: 99%

“…The surveys mentioned (Chung 2011;Li et al 2014;Pérez-Lombard et al 2009) included discussion of various statistical approaches and the use of measured energy consumption for benchmarking. However, these approaches do not include a way to select the regression model that is best suited for a specific benchmarking scenario.…”

Section: Related Workmentioning

confidence: 99%

“…This approach incorporates occupants' behavior because it uses actual energy consumption data: more economical behavior results in a better score. Statistical approaches are commonly used for benchmarking existing buildings (Li et al 2014). The Energy Slices approach proposed in this study also belongs to this category.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy slices: benchmarking with time slicing

et al. 2017

View full text Add to dashboard Cite

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants' wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. Selecting the regression model based on actual data would ensure that the model fits the data well. Consequently, this paper proposes Energy Slices, an energy benchmarking approach with time slicing for existing buildings. Time slicing enables separation of time periods with different consumption patterns. The regression model suited for the specific scenario is selected using cross validation, which ensures that the model performs well on previously unseen data. The evaluation is carried out on a case study involving two sports arenas; event energy efficiency is benchmarked to identify low-performing events. The case study demonstrates the Energy Slice procedure and shows the importance of model selection.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy slices: benchmarking with time slicing

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Also discussed are future trends in Data Science, which will lead to new methods and tools capable of the more intelligent processing of large amounts of data collected from multiple distributed devices. Although there are other reviews on automatic techniques for building efficiency assessment [4,5], and on classification methods for load and energy consumption prediction [6], this work examines and discusses a broader set of data science techniques, and their applications to the different aspects of building energy management.…”

Section: Introductionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

227

108

View full text Add to dashboard Cite

The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

show abstract

References

2018

Building Performance Analysis

View full text Add to dashboard Cite

Methods for benchmarking building energy consumption against its past or intended performance: An overview

Cited by 168 publications

References 39 publications

Energy slices: benchmarking with time slicing

Energy slices: benchmarking with time slicing

Data science for building energy management: A review

References

Contact Info

Product

Resources

About