Development of a Typical Hourly Electricity Consumption Profile for Student Residence Halls Based on Central Tendency Method

K. P. Amber, W. Aslam, M. A. Bashir


Actual measured hourly electricity consumption profile (HEP) helps the building engineer in numerous ways, e.g. for the optimum and accurate sizing of a solar PV for their building, for identifying abnormal peaks and drops, for negotiating with utility supplier etc. Conventional electricity meters do not have features to provide hourly consumption; instead these provide daily or monthly consumption. In such situation, it becomes difficult to make a precise estimate of buildings hourly consumption and a reliable and quick method is desired to estimate hourly consumption. Using four years of measured hourly electricity consumption data for three residence halls, this paper aims to use the central tendency method to develop a dimensionless typical HEP for this building category. Based on the skewed nature of hourly data distributions, median was selected as suitable measure of central tendency. Therefore, using the hourly median values, a typical HEP was developed. The proposed HEP was tested and compared with the actual HEPs of three other similar buildings and it was found that the estimated HEPs matched very well with actual HEPs with a maximum hourly RMSE of 0.9%. Finally, limitations of the proposed HEP are discussed and some recommendations are made in this regard.

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HEFCE (Higher Education Funding Council for England), Carbon Reduction Target and Strategy for Higher Education in England, 2010 [Online] Available at: pubs/hefce/ 2010/10_01/10_01a.pdf.

K.P Amber. and J. Parkin, Barriers to the uptake of combined heat and power technology in the UK higher education sector, Int. J. Sustain. Energ., vol. 34, No. 6, pp. 406-416, 2015.

K.P. Amber, Development of a Combined Heat and Power Sizing Model for the Higher Education Sector of the United Kingdom, (Unpublished doctoral thesis), London South Bank University, London, United Kingdom, 2013.

A. Saush, Personal communication with Energy Manager of London South Bank University, June 17, 2011.

S. Manikandan, Measures of central tendency, J. Pharmacol. Pharmacother., vol. 2, no. 2, pp. 140-142, 2011.

B, Dawson and R.G. Trapp, Basic and Clinical Biostatistics. 4th ed. New York: Mc-Graw Hill, 2004.

T.D. Swinscow and M.J. Campbell, Statistics at square one, 10th Ed. New Delhi, India, Viva Books Private Ltd, 2003.

Laerd Statistics, FAQs - Measures of Central Tendency, 2014 [Online] Available at: https:// stat

NIST/SEMATECH, E-Handbook of Statistical Methods, 2012 [Online] Available at: http:// [10] F.J. Gravetter and L.B. Wallnau, Statistics for the behavioral sciences 5th Ed. Belmont: Wadsworth Thomson Learning, 2000.

A. Petrie and C. Sabin, Medical statistics at a glance 3rd Ed. Oxford: Wiley-Blackwell, 2009.

S. Manikandan, Measures of central tendency: Median and mode, Pharmacol. Pharmacother., vol. 2, no. 3, 214-215, 2011.

Vitutor, Notes 2, Mode, 2010 [Online] Available at:

J. Zhao, Y. Xin and D. Tong, Energy consumption quota of public buildings based on statistical analysis, Energy Policy, vol. 43, pp. 362-370, 2012.

K.R. Sundaram, S.N. Dwivedi and V. Sreenivas, Medical statistics principles and methods, 1st Ed. New Delhi: B.I Publications Pvt. Ltd, 2010.

B. Dawson and R.G.Trapp RG. Basic and Clinical Biostatistics 4th ed. New York: Mc-Graw Hill, 2004.

Wikipedia, Median, 2014 [Online] Available at: http://en. /wiki/Median.

T. Sharp, Energy benchmarking in commercial-office buildings. Proc. of the 1996 ACEEE Summer Study on Energy-Efficiency in Buildings, American Council for an Energy-Efficient Economy, Washington, DC, vol. 4, , pp. 321-329, 1996.

M. Hubert and E. Vandervieren, An adjusted box plot for skewed distributions, Comput. Stat. Data An., vol. 52, No. 12, pp. 5186-5201, 2008.

G. Marshall and L. Jonker, An introduction to descriptive statistics: A review and practical guide, Review Article, Radiography, vol. 16, No. 4, pp. 1-7, 2010.

J. Keirstead, Benchmarking urban energy efficiency in the UK, Energy Policy, vol. 63, pp. 575-587, 2013.

V.M. Nik, A.S. Kalagasidisa and E. Kjellstrmb, Statistical methods for assessing and analysing the building performance in respect to the future climate, Build. Environ., vol. 53, pp-107-118, 2012.

K.P. Amber, M.W. Aslam and K. Hussain, Electricity consumption forecasting models for administration buildings of the UK Higher Education sector Energ. Buildings, vol. 90, pp. 127-136, 2015.

ABS, Australian Bureau of Statistics, Frequency Distribution [Online] Available from: a3121120.nsf/home.

I. Ward, A. Ogbanna and A. Altan, Sector review of UK higher education energy consumption, Energy Policy, vol. 36, pp. 2939- 2949, 2008.

S. Katipamula, T.A. Reddy and D.E. Claridge, Bias in predicting annual energy use in commercial buildings with regression models developed from short datasets Pacific Northwest Laboratory, K5-20, P.O Box 999, Richland, Washington 99352, 1994.

Mathbits, Correlation Coefficient Available at: http://mathbits. com/MathBits/TISection/Statistics2/correlation.htm.

K.P. Amber et al. / The Nucleus 53, No. 1 (2016) 14-25

QMUL, Queen Mary University of London, Mile End Accommodation [Online] Available from: http://www.residences.

ERG, Environmental Research Group, Kings College London, Data download. [Online]. Available at: http://www.londonair. [Accessed on] November 25, 2012.

Korolija, Y. Zhang, L.M. Halburd and V.I. Hanby, Regression models for predicting UK office building energy consumption from heating and cooling demands, Energ. Buildings, vol. 59, pp. 214227, 2013.

V. Bianco, O. Manca and S. Nardini, Electricity consumption forecasting in Italy using linear regression models, Energy, vol. 34, pp. 1413-1421, 2009.

C. Noren and J. Pyrko, Using Multiple Regression Analysis to Develop Electricity Consumption Indicators for Public Schools, Lund Institute of Technology, Sweden, 1997, [Online]. Available at:

R. Edwards, J. New and L.E. Parker, Predicting future hourly residential electrical consumption: A machine learning, Energ. Buildings, vol. 49, pp. 591-603, 2012.


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