Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy

Yaqub Rafiq, Shen Wei, Robert Guest, Robert Stone, Pieter de Wilde

2014

Abstract

In this paper Artificial Neural Networks (ANNs) is used to model effects of various human behaviour on energy consumption of the residential buildings in the UK. A virtual model of a bungalow has been developed in which various aspects of the, physical changes in the building such as wall and floor insulation, single and double glazing combined by the human behaviour aspects such as thermostat setting, various periods of door and/or window opening etc. are modelled using EnergyPlus software for evaluating energy consumption for a combination of scenarios. ANNs were then used to learn the effects of various human behaviours on energy consumption. The results demonstrated that the ANN is capable of learning the effects that changes in the human behaviour have in evaluating energy saving in residential buildings and it generated results very quickly for unseen cases.

References

  1. DECC, United Kingdom housing energy fact file, 2013.
  2. Haas, R., H. Auer, and P. Biermayr, The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, 1998. 27(2): p. 195-205.
  3. Al-Mumin, A., O. Khattab, and G. Sridhar, Occupants' behavior and activity patterns influencing the energy consumption in the Kuwaiti residences. Energy and Buildings, 2003. 35(6): p. 549-559.
  4. GOV. Green Deal. 2014; Available from: https://www.gov.uk/green-deal-energy-savingmeasures/overview.
  5. TEDDINET. TEDDINET website. 2014; Available from: www.teddinet.org.
  6. de Wilde, P., et al., Using building simulation to drive changes in occupant behaviour: A pilot study, in Building Simulation 2013 Conference2013: Chambery, France.
  7. Love, J., Mapping the impact of changes in occupant heating behaviour on space heating energy use as a result of UK domestic retrofit, in Retrofit 20122012: Manchester, UK, 22- 26 January.
  8. Kim, Y. K. and H. Altan, Using dynamic simulation for demonstrating the impact of energy consumption by retrofit and behavioural change in Building Simulation 2013 Conference2013: Chambery, France.
  9. Wei, S., R. Jones, and P. de Wilde, Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 2014. 70(0): p. 36-44.
  10. Fabi, V., et al., Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models. Building and Environment, 2012. 58(0): p. 188-198.
  11. Gardner, G.T. and P.C. Stern, The Short List: The Most Effective Actions U.S. Households Can Take to Curb Climate Change. Environment: Science and Policy for Sustainable Development, 2008. 50(5): p. 12-25.
  12. DesignBuilder. DesignBuilder website. 2014; Available from: http://www.design builder.co.uk/.
  13. DOE. EnergyPlus Energy Simulation Software 2014; Available from: v jkhttp://apps1.eere. energy.gov/buildings/energyplus/.
  14. Kalogirou, S., Artificial neural networks in energy applications in buildings. International Journal of Low Carbon Technologies, 2006. 1(3).
  15. Azadeh, A., S.F. Ghaderi, and S. Sohrabkhani, Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 2008. 49(8): p. 2272-2278.
  16. Swan, L.G., V.I. Ugursal, and I. Beausoleil-Morrison, Occupant related household energy consumption in Canada: Estimation using a bottom-up neural-network technique. Energy and Buildings, 2011. 43(2-3): p. 326-337.
  17. Luo, X., et al., A fuzzy neural network model for predicting clothing thermal comfort. Computers & Mathematics with Applications, 2007. 53(12): p. 1840-1846.
  18. Kalogirou, S.A., Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks. Applied Energy, 2000. 66(1): p.63-74.
  19. Kalogirou, S.A., S. Panteliou, and A. Dentsoras, Artificial neural networks used for the performance prediction of a thermosiphon solar water heater. Renewable Energy, 1999. 18(1): p. 87-99.
  20. Lee, W.-Y., J.M. House, and N.-H. Kyong, Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy, 2004. 77(2): p. 153-170.
  21. Curtiss, P.S., M.J. Brandemuehl, and J.F. Kreider, Energy management in central HVAC plants using neural networks. ASHRAE Transactions, 1994.
  22. Rafiq, M.Y., G. Bugmann, and D.J. Easterbrook, Neural network design for engineering applications. Computers & Structures, 2001. 79(17): p. 1541-1552.
  23. Werbos, P.J., Beyond Regression: New tools for prediction and analysis in the behavioural science, 1974, Harvard University.
  24. MathWorks. Neural Network Toolbox. 2014; Available from: http://www.mathworks. co.uk/products/neural-network/.
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Paper Citation


in Harvard Style

Rafiq Y., Wei S., Guest R., Stone R. and de Wilde P. (2014). Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy . In Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014) ISBN 978-989-758-041-3, pages 3-10. DOI: 10.5220/0005049600030010


in Bibtex Style

@conference{anniip14,
author={Yaqub Rafiq and Shen Wei and Robert Guest and Robert Stone and Pieter de Wilde},
title={Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy},
booktitle={Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014)},
year={2014},
pages={3-10},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005049600030010},
isbn={978-989-758-041-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2014)
TI - Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy
SN - 978-989-758-041-3
AU - Rafiq Y.
AU - Wei S.
AU - Guest R.
AU - Stone R.
AU - de Wilde P.
PY - 2014
SP - 3
EP - 10
DO - 10.5220/0005049600030010