Longitudinal analysis of energy metering data from non-domestic buildings

Brown, Neil; Wright, A. J.; Shukla, Ashish; Stuart, Graeme

doi:10.1080/09613210903374788

Cited by 29 publications

(17 citation statements)

References 4 publications

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“…However, longitudinal performance is affected by factors such as building occupancy, deterioration of physical elements, climatic conditions, and building maintenance processes and policies (de Wilde et al, 2011). Brown et al (2010) present a longitudinal analysis of 25 buildings in the UK and found an increase of 9% in energy use on average per year over 7 years, with a SD of 18%. Similarly, Piette et al (1994) analyzed 28 buildings in the US and found an average increase of 6% between the third and fourth year, with no average increase during the fifth year.…”

Section: Longitudinal Variability In Operationmentioning

confidence: 99%

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

et al. 2016

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This paper reviews the discrepancy between predicted and measured energy use in non-domestic buildings in a UK context with outlook to global studies. It explains differences between energy performance quantification and classifies this energy performance gap as a difference between compliance and performance modeling with measured energy use. Literary sources are reviewed in order to signify the magnitude between predicted and measured energy use, which is found to deviate by +34% with a SD of 55% based on 62 buildings. It proceeds in describing the underlying causes for the performance gap, existent in all stages of the building life cycle, and identifies the dominant factors to be related to specification uncertainty in modeling, occupant behavior, and poor operational practices having an estimated effect of 20-60, 10-80, and 15-80% on energy use, respectively. Other factors that have a high impact are related to establishing the energy performance target, such as early design decisions, heuristic uncertainty in modeling, and occupant behavior. Finally, action measures and feedback processes in order to reduce the performance gap are discussed, indicating the need for energy in-use legislation, insight into design stage models, accessible energy data, and expansion of research efforts toward building performance in-use in relation to predicted performance. Keywords: energy performance gap, energy use in buildings, predictions, measurements, feedback, postoccupancy evaluation HiGHLiGHTS 1. Classifies the performance gap and analyses its magnitude and underlying causes. 2. The regulatory energy gap is found to deviate by +34% with a SD of 55% based on 62 case study buildings. 3. Specification uncertainty, occupant behavior, and poor practice are dominant underlying causes with an estimated effect of 20-60, 10-80, and 15-80% on energy use, respectively. 4. Action measures to reduce the energy performance gap in contrast to the building life cycle are discussed. 5. There is a need to develop techniques to mitigate the magnitude and underlying causes of the performance gap.

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Section: Longitudinal Variability In Operationmentioning

confidence: 99%

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

et al. 2016

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“…For example, Brown, et al [14] diagnosed the operational schedule problem of lighting using hourly electricity data, but the monitoring structure and energy model were not mentioned. Dong, et al [15] tried to find the linear relationship between energy consumption and climate parameters.…”

Section: Necessity Of a Data Modelmentioning

confidence: 99%

Comparison of building energy use data between the United States and China

Xia

Hong

Shen

et al. 2014

Energy and Buildings

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Buildings in the United States and China consumed 41% and 28% of the total primary energy in 2011, respectively. Good energy data are the cornerstone to understanding building energy performance and supporting research, design, operation, and policy making for low energy buildings. This paper presents initial outcomes from a joint research project under the U.S.-China Clean Energy Research Center for Building Energy Efficiency. The goal is to decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. This paper first reviews and compares several popular existing building energy monitoring systems in both countries. Next a standard energy data model is presented. A detailed, measured building energy data comparison was conducted for a few office buildings in both countries. Finally issues of data collection, quality, sharing, and analysis methods are discussed. It was found that buildings in both countries performed very differently, had potential for deep energy retrofit, but that different efficiency measures should apply.

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“…Degree-days provide a mean to compare energy performance in buildings under different conditions. Analysis techniques also use degree-days to produce empirical models of energy consumption [22]. The original Degree days concepts originated mainly within agricultural research to define the variation in outdoor air temperature [23].…”

Section: Degree Day For Heating and Coolingmentioning

confidence: 99%

A Database for Climatic Conditions around Europe for Promoting GSHP Solutions

et al. 2018

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Abstract:Weather plays an important role for energy uses in buildings. For this reason, it is required to define the proper boundary conditions in terms of the different parameters affecting energy and comfort in buildings. They are also the basis for determining the ground temperature in different locations, as well as for determining the potential for using geothermal energy. This paper presents a database for climates in Europe that has been used in a freeware tool developed as part of the H2020 research project named "Cheap-GSHPs". The standard Köppen-Geiger climate classification has been matched with the weather data provided by the ENERGYPLUS and METEONORM software database. The Test Reference Years of more than 300 locations have been considered. These locations have been labelled according to the degree-days for heating and cooling, as well as by the Köppen-Geiger scale. A comprehensive data set of weather conditions in Europe has been created and used as input for a GSHP sizing software, helping the user in selecting the weather conditions closest to the location of interest. The proposed method is based on lapse rates and has been tested at two locations in Switzerland and Ireland. It has been demonstrated as quite valid for the project purposes, considering the spatial distribution and density of available data and the lower computing load, in particular for locations where altitude is the main factor controlling on the temperature variations.

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Longitudinal analysis of energy metering data from non-domestic buildings

Cited by 29 publications

References 4 publications

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

Comparison of building energy use data between the United States and China

A Database for Climatic Conditions around Europe for Promoting GSHP Solutions

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