Abstract
This is an informal introduction to some aspects of energy system optimisation to provide sustainable services to people in dwellings. This paper advances data, methods and results of optimising building energy systems and controls for energy and environment policy using quantitative techniques. Optimisation can aid the design of systems to meet policy objectives efficiently and at low cost. Three optimisation methods were applied: genetic algorithm (GA), particle swarm optimisation (PSO) and steepest decent (SD). It was concluded that the higher the energy price, the greater the efficiency of the dwelling envelope and heating system to achieve least cost. Ultimately, optimisation should be done across all systems and stock, and simultaneously for configuration, size and controls.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, R., Christensen, C., Horowitz, S.: Program Design Analysis using BEopt Building Energy Optimization Software: Defining a Technology Pathway Leading to New Homes with Zero Peak Cooling Demand. Presented at the 2006 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, California, August 13-18 (2006)
DECC, Statistical Release: Provisional Figures for 2010, National Statistics Information on (2011), www.communities.gov.uk (last accessed July 2012)
DTI, Meeting the Energy Challenge, A White Paper on Energy, CM 714, The Stationery Office (2007), http://www.decc.gov.uk/assets/decc/publications/white_paper_07/file39387.pdf (last accessed July 2012)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE International Conference on Evolutionary Computation, vol. 1, pp. 81–86 (2001)
European Parliament and Council, 2009/28/EC on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC (2009)
HM Government, The Carbon Plan: Delivering our low carbon future. Amended 2nd December 2011 from the version laid before Parliament on (December 1, 2011), www.official-publications.gov.uk (accessed July 2012)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Klein, L., Kavulya, G., Jazizadeh, F., Kwak, J., Becerik-Gerber, B., Varakantham, P., Tambe, M.: Towards Optimization Of Building Energy And Occupant Comfort Using Multi-Agent Simulation. Automation in Construction 22, 525–536 (2012)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation Anchorage, vol. 6, pp. 9–73 (1998)
US Department of Energy (USDOE), EnergyPlus Energy Simulation Software (2012), http://apps1.eere.energy.gov/buildings/energyplus/ (accessed July 2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barrett, M., Spataru, C. (2013). Optimizing Building Energy Systems and Controls for Energy and Environment Policy. In: Hakansson, A., Höjer, M., Howlett, R., Jain, L. (eds) Sustainability in Energy and Buildings. Smart Innovation, Systems and Technologies, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36645-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-36645-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36644-4
Online ISBN: 978-3-642-36645-1
eBook Packages: EngineeringEngineering (R0)