Evaluating energy performance in non-domestic buildings: A review

Borgstein, EH Edward; Lamberts, Roberto; Hensen, Jlm Jan

doi:10.1016/j.enbuild.2016.07.018

Cited by 127 publications

(68 citation statements)

References 148 publications

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“…Recent works using the most popular type of monitoring for energy disaggregation, non-intrusive load monitoring (NILM), are surveyed in detail in [5], [6]. [7] discusses recent approaches for benchmarking energy in non-residential buildings, while [8] presents a collection of performance analysis tools and algorithm benchmarking for non-residential buildings.…”

Section: Previous Workmentioning

confidence: 99%

A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure

Mylonas

Amaxilatis

Tsampas

et al. 2019

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)

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A considerable part of recent research in smart cities and IoT has focused on achieving energy savings in buildings and supporting aspects related to sustainability. In this context, the educational community is one of the most important ones to consider, since school buildings constitute a large part of non-residential buildings, while also educating students on sustainability matters is an investment for the future. In this work, we discuss a methodology for achieving energy savings in schools based on the utilization of data produced by an IoT infrastructure installed inside school buildings and related educational scenarios. We present the steps comprising this methodology in detail, along with a set of tangible results achieved within the GAIA project. We also showcase how an IoT infrastructure can support activities in an educational setting and produce concrete outcomes, with typical levels of 20% energy savings.

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Section: Previous Workmentioning

confidence: 99%

A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure

Mylonas

Amaxilatis

Tsampas

et al. 2019

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)

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“…As mentioned earlier, the building regulatory energy performance gap highlights the mismatch between the compliance modelling tools predictions at the design stage and the reported measured data from the site. While researchers have studied energy performance gap in various resolutions, the majority of studies have focused on the building annual energy consumption being the key factor in assessing regulatory performance gap [34]. In this study, we will also consider the overall annual energy consumption as a factor for performance gap assessment in addition to the energy consumption breakdown into heating, electricity, lighting and ventilation consumption.…”

Section: Regulatory Performance Gapmentioning

confidence: 99%

ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps

Jradi

Arendt

Sangogboye

et al. 2018

Energy and Buildings

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“…These are offices, hotels, banks and agencies, and mixed use industrial, but there is no specific guidance for University buildings (CIBSE, 2006). The International Performance Measuring and Verification Protocol (IPMVP) method is adopted predominately in the United States (Borgstein et al, 2016) and is used in locations where shared savings schemes are agreed, as a way of quantifying the in-use consumption of systems and buildings. However, it is suggested that the cost of IPMVP measuring and verification should not exceed 10% of the estimated savings and be appropriate for the scale of the project.…”

Section: Building Performance Methodsmentioning

confidence: 99%

Determining the Difference between Predicted vs. Actual Lighting Use in Higher Education Corridors

2017

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The performance gap is a measure of the difference between design assumptions and actual in field data. Estimating terms, specifically the operational hours of use, when making such design assumptions in order to predict the impact of lighting upgrades can potentially result in either an overestimate or underestimate of the savings to be made. In this paper, the background of performance gap measurement is outlined and field measurements are gathered and applied retrospectively to lighting upgrades in corridors. The lighting upgrade projects in three university buildings and their assumptions are explained in relation to the operational hours proposed using the industry "Energy assessment and reporting method." We then describe a simple and relatively inexpensive means of taking in field measurements using small unobtrusive environmental loggers to record the lighting use and occupancy. This method, which can be implemented prior to upgrade works or energy efficiency retrofits, reveals substantially different patterns of annual electricity consumption, and carbon dioxide equivalent emissions from those assumed by a priori estimates. Patterns of use emerge that include additional hours of use by cleaners, out of hours working, and weekend working not anticipated in original estimates. These results also suggest a valuable distinction between lighting on hours and occupancy hours not captured in the current "Energy assessment and reporting method." The results find the differences between predicted vs. actual data are considerably different, with lights on hours ranging from −67% lower to 25% and 138% higher when compared with predicted operational hours. We conclude that the estimates the industry uses in calculating energy efficiency upgrades should be accompanied by clear and adequate information about occupancy use. In our study, the consequence of reporting energy savings using assumptions and estimates in calculations resulted in a substantial overall underestimate of the savings achieved in practice.

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Evaluating energy performance in non-domestic buildings: A review

Cited by 127 publications

References 148 publications

A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure

A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure

ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps

Determining the Difference between Predicted vs. Actual Lighting Use in Higher Education Corridors

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