A visual energy performance assessment and decision support tool for dwellings

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• 作者：Amit Mhalas (1)
  Mohamad Kassem (1)
  Tracey Crosbie (1)
  Nashwan Dawood (1)
  
• 关键词：Geographic Information System (GIS) ; Domestic energy assessment ; Standard Assessment Procedure (SAP)
• 刊名：Visualization in Engineering
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：1
• 期：1
• 全文大小：1232KB
• 参考文献：1. Akash BA, Mamlook R, Mohsen MS: Multicriteria selection of electric power plants using analytical hierarchy process. / Electric Power Systems Research 1999,52(1):29鈥?5. CrossRef
  2. All Party Urban Development Group: / Greening UK cities鈥?buildings. London, UK: All Party Urban Development Group; 2008.
  3. Boardman B: / Home Truths: A low carbon strategy to reduce UK housing emissions by 80% by 2050. Oxford, UK: University of Oxford鈥檚 Environmental Change Institute; 2007.
  4. Boardman B, Darby S, Killip G, Hinnels M, Jardine CN, Palmer J: / 40% house. Oxford, UK: University of Oxford鈥檚 Environmental Change Institute; 2005.
  5. Cheng V, Steemers K: Modelling domestic energy consumption at district scale: A tool to support national and local energy policies. / Environmental Modelling & Software 2011, 26:1186鈥?198. CrossRef
  6. Communities and Local Government: / English Housing Survey 2010鈥?1. London, UK: Department of Communities and Local Government; 2012.
  7. DECC: / Community Energy Saving Programme. Department of Environment and Climate Change Consultation; 2009.
  8. DECC: / Evaluation synthesis of energy supplier obligation policies. London, UK: Department of Energy & Climate Change; 2011.
  9. DECC: / The Green Deal and Energy Company Obligation. London, UK: Department of Energy & Climate Change; 2011.
  10. DECC: Wind Speed Database. 2013.
  11. DECC/BRE: / The Government's Standard Assessment Procedure for Energy Rating of Dwellings. Watford: Building Research Establishment; 2010.
  12. Environmental Systems Research Institute: A quick tour of what's new in ArcGIS 10. 2010.
  13. Erol O, Kilkis B: An energy source policy assessment using analytical hierarchy process. / Energy Conversion and Management 2012, 63:245鈥?52. CrossRef
  14. Firth SK, Lomas KJ, Wright AJ: Targeting household energy-efficiency measures using sensitivity analysis. / Building Research and Information 2010,38(1):24鈥?1.
  15. Fung AS: / Modeling of National and regional energy consumption and associated greenhouse gas emissions. PhD Thesis. Dalhousie University; 2003.
  16. Gamboa G, Munda G: The problem of windfarm location: a social multi-criteria evaluation framework. / Energy Policy 2007,35(3):1564鈥?583. CrossRef
  17. Georgopoulu E, Sarafidis Y, Mirasgedis S, Zaimi S, Lalas DP: A multiple-criteria decision-aid approach in defining national priorities for greenhouse gases emissions reduction in the energy sector. / Europen Journal of Operational Research 2003, 146:199鈥?15. CrossRef
  18. Goodchild MF: / Geographic information systems and science: Today and tomorrow. Xuzhou: Procedia Earth and Planetary Science; 2009:1037鈥?043.
  19. Gupta R: Moving towards low-carbon buildings and cities: experiences from Oxford, UK. / International Journal of Low-Carbon Technologies 2009, 4:159鈥?68. CrossRef
  20. H.M. Government: / Climate Change Act. London: The Stationery Office Limited; 2008.
  21. Haas R, Schipper L: Residential energy demand in OECD-countries and the role of irreversible efficiency improvements. / Energy Economics 1998,20(4):421鈥?42. CrossRef
  22. HEED: / Area Summary Report (Middlesbrough, Middlesbrough South and East Cleveland). Energy Savings Trust; 2012.
  23. Hirst E, Lin W, Cope J: A residential energy use model sensitive to demographic, economic, and technological factors. / The Quarterly Review of Economics and Finance 1977,17(2):7鈥?2.
  24. Hourcade JC, Jaccard M, Bataille C, Gershi F: Hybrid modeling: new answers to old challenges. / Energy Journal 2006, (Special Issue):1鈥?2.
  25. House of Lords: / The EU鈥檚 Target for Renewable Energy: 20% by 2020. London: The Stationery Office Limited; 2008.
  26. Jaccard M, Bailie A, Nyboer J: CO ₂ emission reduction costs in the residential sector: Behavioral parameters in a bottom-up simulation model. / Energy Journal 1996,17(4):107鈥?34.
  27. Johnston D: / A physically based energy and carbon dioxide emission model of the UK housing stock. Leeds, UK: Leeds Metropolitan University; 2003.
  28. Jones P, Patterson J, Lannon S: Modelling the built environment at an urban scale - energy and health impacts in relation to housing. / Landscape and Urban Planning 2007, 83:39鈥?9. CrossRef
  29. Kassem M, Dawood N, Mitchell N: A decision support system for the selection of curtain wall systems at the design development stage. / Construction Management and Economics 2012, 30:1039鈥?053.
  30. Kavgic M, Mavrogianni A, Mumovic D, Summerfield A, Stevanovic Z, Djurovic-Petrovic M: A review of bottom-up building stock models for energy consumption in the residential sector. / Building and Environment 2010, 45:1683鈥?697. CrossRef
  31. Larsen BM, Nesbakken R: Household electricity end-use consumption: results from econometric and engineering models. / Energy Economics 2004,26(2):179鈥?00. CrossRef
  32. Natarajan S, Levermore GJ: Domestic futures-Which way to a low-carbon housing stock? / Energy Policy 2007,35(11):5728鈥?736. CrossRef
  33. Office of the Deputy Prime Minister: / Planning Policy Statement 12: Local Development Frameworks. London: Stationary Office; 2010.
  34. OFGEM: / Typical domestic energy consumption figures. London, UK: OFGEM; 2011.
  35. OS MasterMap Address Layer: / Ordinance Survey, GB . Middlesbrough: Coverage; 2010.
  36. OS MasterMapTopography Layer: / Ordinance Survey, GB . Middlesbrough: Coverage; 2010.
  37. Palmer J, Cooper I: / Great Britain鈥檚 housing energy fact file. London, UK: Department of Energy and Climate Change; 2011.
  38. Pohekar SD, Ramachandran M: Application of multi-criteria decision making to sustainable energy planning - A review. / Renewable and Sustainable Energy Reviews 2004, 8:365鈥?81. CrossRef
  39. Rylatt RM, Gadsden SJ, Lomas KJ: Methods of predicting urban domestic energy demand with reduced datasets : a review and a new GIS based approach. / Building Services Engineering Research and Technology 2003,24(2):93鈥?02. CrossRef
  40. Saaty TL: / The analytic hierarchy process. New York: Macgraw-Hill; 1980.
  41. Shorrock LD, Dunster JE: The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock. / Energy Policy 1997,25(12):1027鈥?037. CrossRef
  42. Swan LG, Ugursal VI: Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. / Renewable and Sustainable Energy Reviews 2009, 13:1819鈥?835. CrossRef
  43. Tuladhar SD, Yuan M, Bernstein P, Montgomery WD, Smith A: A top-down bottom up modelling approach to climate change policy analysis. / Energy Economics 2009,31(Supp. 2):S223-S234. CrossRef
  44. Wang JJ, Jing YY, Zhang CF, Zhao JH: Review on multi-criteria decision analysis aid in sustainable energy decision-making. / Renewable and Sustainable Energy Reviews 2009, 13:2263鈥?278. CrossRef
  45. Wilson D, Swisher J: Exploring the gap. Top-down versus bottom-up analyses of the cost of mitigating global warming. / Energy Policy 1993,21(3):249鈥?63. CrossRef
  46. Wright A: What is the relationship between built form and energy use in dwellings? / Energy Policy 2008, 36:4544鈥?547. CrossRef
  
• 作者单位：Amit Mhalas (1)
  Mohamad Kassem (1)
  Tracey Crosbie (1)
  Nashwan Dawood (1)
  
  1. Technology Futures Institute, Teesside University, Middlesbrough, TS1 3BA, UK
  
• ISSN：2213-7459

文摘

Background The target for carbon dioxide (CO2) emissions reduction in the UK is set at 20% by 2020 and 80% by 2050. The UK housing stock is one of the least energy efficient in Europe. The energy used in homes accounts for more than a quarter of energy use and carbon dioxide emissions in Great Britain. Therefore, it is imperative to improve the energy performance of the existing housing stock and fully exploit energy efficiency and renewable energy interventions. The UK has developed several policies and initiatives to improve the energy performance of the housing stock and there are a number of databases that hold information about the condition of the housing stock. However, existing approaches and tools do not allow decision makers to assess the environmental and economic effectiveness of CO2 reduction strategies at the neighbourhood level. Methods This research presents a methodology that integrates these energy databases with visualisation systems and multi-criteria decision analyses to enable the evaluation of the environmental and financial implications of various energy efficiency and renewable energy interventions at both building and neighbourhood levels. The methodology is prototyped in a proof-of-concept tool which is validated and tested in an empirical case study with local authorities and social housing providers. Results The validation study compared the energy performance of the dwellings estimated by the proposed methodology with the energy performance calculated from actual survey and confirmed that the results are consistent. The case study demonstrated that the methodology and the prototype can be reliably utilised to evaluate the environmental and financial implications of various energy efficiency and renewable energy interventions. Conclusion The findings illustrate that the tool is particularly useful for town planners, local authorities and social housing providers. They can make informed decisions about the implementation of energy policies and initiatives along with energy suppliers, building engineers and architects. The tool developed in the research and presented in this paper can contribute to meeting CO2 emission reduction targets.