Novel instrumentation for monitoring after-hours electricity consumption of electrical equipment, and some potential savings from a switch-off campaign

Brown, Neil; Bull, R.K.; Faruk, Farhan; Ekwevugbe, Tobore

doi:10.1016/j.enbuild.2011.11.023

Cited by 26 publications

(10 citation statements)

References 5 publications

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“…However, a wider literature review, for example of the risk communication literature, shows that expert-led, topdown techno-centric solutions rarely deliver on their promises (Fioriono, 1990;Renn 1992;Leach, Scoones et al, 2010). Staying with buildings for a moment, user-experience and the literature tells us that around 30% of energy in buildings is wasted through the behaviour of the building-users (Brown, Bull et al, 2012). Such waste is in part due to the technical limitations and flaws inherent within building energy management system (BEMS) such as the reliability of sensors, the quality of algorithms alongside human error.…”

Section: Smart Cities -A Review Of Definitions and Conceptsmentioning

confidence: 99%

Smart citizens for smart cities: participating in the future

Bull¹,

Azennoud²

2016

Proceedings of the Institution of Civil Engineers - Energy

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Section: Smart Cities -A Review Of Definitions and Conceptsmentioning

confidence: 99%

Smart citizens for smart cities: participating in the future

Bull¹,

Azennoud²

2016

Proceedings of the Institution of Civil Engineers - Energy

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“…Various methods, including static [28] and dynamic [29] methods, were adopted in research to obtain building usage data, especially occupant-related information. After Ekwevugbe et al [30] measured the occupancy in non-domestic buildings and added the information to the building management system.…”

Section: Building Usage Data Collectionmentioning

confidence: 99%

An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design

et al. 2018

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Given continued improvement in the thermal performance of building envelopes, interior disturbances caused by occupant behavior now have the greatest impact on building loads and energy consumption. The accurate calculation of interior load during design stage was emphasized in this paper, and a new method was proposed. Indoor occupants were considered as the core of interior disturbances, and the relationship with other interior disturbances was explored. The interior heat release was arbitrarily combined with the representative cooling load to be utilized in building cooling load calculation. Field surveys were conducted in three typical university buildings: an office building, a teaching building, and a library, located in a university in Tianjin, China. The oversized chillers supplying cooling for the buildings resulted from the overestimating of the indoor occupant number and the power density of electric appliances. Through quantitative analysis, it was observed that the maximum representative interior loads were 196.43, 329.94, and 402.58 W/person, respectively, for the case buildings, at least 50% less than the empirical design data. Compared to the measured cooling load during the testing period, the accuracy of the modified cooling load was greater than 90%. This research is intended to serve as a reference for calculating and optimizing the design loads of cooling systems.

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“…This technique is used in the absence of sub-metered interval data for a large building set. Sub-metering at one office building requires expensive installation of end-use sensors [9][10][11], costing around $100,000 for a medium sized office building [10], and still contains significant uncertainties due to sensor failures and other errors in sensor accuracy or installation [12]. Large datasets of sub-metered energy use do not exist due to the costs and effort associated with data collection, such as normalizing for operation, maintenance, and controls, as well as remaining significant uncertainties in comparing data sets from different investigators.…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis of simulated energy use for LEED certified U.S. office buildings

Heidarinejad

Dahlhausen

McMahon

et al. 2014

Energy and Buildings

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This study uses cluster analysis to examine simulated energy consumption of 134 U.S. LEED NC office buildings to classify buildings into high, medium, and low energy use intensity clusters. The analysis uses energy simulation results from the LEED database, as a comparably large data set of energy end uses from sub-meter data does not yet exist. The difference between the low energy use cluster and other clusters is explained mostly by lower process loads and lower heating energy intensity, and partly by lower intensities of other HVAC related end-uses. Lighting energy use shows the least variation between clusters. The lower heating energy intensity in the low intensity cluster is largely explained by lower roof U-values, lower window-to-wall ratio, and smaller building size. Unregulated process loads are the most significant contributor to total building energy use, accounting for 36%, 33%, and 31% of the energy use in the high, medium, and low intensity clusters, respectively. This analysis provides a quantitative evaluation of the large difference in energy intensities in high-performance office buildings, showing that these buildings are dominated by internal loads, especially unregulated process loads.

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Novel instrumentation for monitoring after-hours electricity consumption of electrical equipment, and some potential savings from a switch-off campaign

Cited by 26 publications

References 5 publications

Smart citizens for smart cities: participating in the future

Smart citizens for smart cities: participating in the future

An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design

Cluster analysis of simulated energy use for LEED certified U.S. office buildings

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