Disaggregating and Forecasting the Total Energy Consumption of a Building: A Case Study of a High Cooling Demand Facility

Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Authors:

• Juliana Barcelos Cordeiro
• Khashayar Mahani
• Farbod Farzan
• Mohsen A. Jafari

Keywords:

• Energy consumption forecasting
• energy efficiency
• load disaggregation
• pattern recognition approach.

References:

[1] U.S. Energy Information Administration. “How much energy is
consumed in residential and commercial buildings in the United States?”
Available at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1
[2] S. Darby, “The effectiveness of feedback on energy consumption.”
Environmental Change Institute, University of Oxford, 2006. Available
at: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.
Visited: September 2015
[3] J. S. John, “Putting energy disaggregation tech to the test,” November,
2013. Greentech Media. Available at:
http://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.
Visited: September 2015
[4] A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, “Non-intrusive load
monitoring approaches for disaggregated energy sensing: a survey,”
Sensors, vol. 12, no. 12, pp. 16838-16866, December 2012.
[5] G. W. Hart, “Nonintrusive appliance load monitoring,” in Proc. of the
IEEE, vol. 80, pp. 1870-1891, December 1992.
[6] M. Baranski, J. Voss, “Non-intrusive appliance load monitoring based
on Optical Sensor,” IEEE Bologna PowerTech Conference, Bologna,
Italy, June 2003. Available at:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732
[7] L. Farinaccio, R. Zmeureanu, “Using a pattern recognition approach to
disaggregate the total electricity consumption in a house into the major
en-uses,” Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,
August 1999.
[8] J. M. Abreu, F. C. Pereira, P. Ferrão, “Using pattern recognition to
identify habitual behavior in residential electricity consumption,”
Elsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.
[9] C. Beckel, L. Sadamori, S. Santini, “Automatic socio-economic
classification of households using electricity consumption data,” in
Proc. of the 4th international conference on future energy systems, New
York, 2013, pp. 75-86.
[10] H. Zhao, F. Magoulès, “A review on the prediction of building energy
consumption,” Elsevier, Renewable and Sustainable Energy Reviews,
vol. 16, no. 6, pp. 3586-3592, August 2012.
[11] G. K. F. Tso, K. K. W. Yau, “Predicting electricity energy consumption:
A comparison of regression analysis, decision tree and neural networks,”
Elsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.
[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, “Operational
planning for multi-building portfolio in an uncertain energy market,”
Elsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015.