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An Integrated Demand and Carbon Impact Forecasting Approach for Residential Precincts

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Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Estimation of the demand of an urban precinct, related to Electricity, Transport, Waste and Water (ETWW), is a necessary step toward the delivery of quality living environments where daily activities can be conducted in a sustainable manner. A forecasting model that concurrently links demand in all four aforementioned domains to carbon emissions can assist planning agencies, infrastructure providers, operators and private developers to deliver low-carbon urban precincts in the future. Integration of modelling methodologies delivers improved ability, accuracy and flexibility when compared to typical forecasting approaches. This chapter details the outcomes of recent research efforts on the development of an integrated ETWW demand estimation tool with detailed scenario forecasting abilities. Focusing on the residential components of the precinct, modelling outputs provide detailed estimations of household demands and resulting carbon impacts across the four domains. Impacts of non-residential land uses including high-value industry, retail, commercial and open space are also considered and reported on. Model users can estimate the carbon impact of resident population changes, various household structure types, carbon-friendly technologies and climate change for precinct locations across Australia . In addition, the tool accounts for interactions with external infrastructure such as transport networks, off-site waste disposal, water supply locations and grid-based energy supply. Forecasting abilities of the model are demonstrated through case-study applications that reflect of ‘what-if’ type scenario investigations, important to policymaking and planning for future urban development. The user is ultimately able to explore combinations to achieve a low-carbon precinct development.

Keywords

  • Precinct
  • Demand
  • Energy
  • Transport
  • Waste
  • Water

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References

  • Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., et al. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102–109.

    CrossRef  Google Scholar 

  • Arbués, F., Garcıa-Valiñas, M. Á., & Martınez-Espiñeira, R. (2003). Estimation of residential water demand: A state-of-the-art review. The Journal of Socio-Economics, 32, 81–102.

    CrossRef  Google Scholar 

  • Citilabs (2011). Discover Cube 5.0. Revision 50-006-0. Citilabs Inc.

    Google Scholar 

  • Commonwealth of Australia (2012). Baseline energy consumption and greenhouse gas emissions, commercial buildings in Australia Part 1—Report, COAG National Strategy on Energy Efficiency. Canberra: Department of Climate Change and Energy Efficiency.

    Google Scholar 

  • Cook, S. H. M., & Gregory, A. (2012). Energy use in the provision and consumption of urban water in Australia: An update. CSIRO Water for a Healthy Country Flagship: Water Services Association of Australia.

    Google Scholar 

  • de Dios Ortúzar, J., & Willumsen L. G. (2011). Modelling Transport, 4th Edition, New York: Wiley.

    Google Scholar 

  • Department of Sustainability, Environment, Water, Population and Communities (2013). A study into commercial & industrial (C&I) waste and recycling in Australia by industry division. Canberra: Government of Australia.

    Google Scholar 

  • DOE (2014). National greenhouse accounts factors. Canberra: Department of the Environment, Commonwealth of Australia.

    Google Scholar 

  • Donkor, E., Mazzuchi, T., Soyer, R., & Roberson, A. J. (2014). Urban water demand forecasting: Review of methods and models. Journal of Water Resources Planning and Management, 140, 146–159.

    CrossRef  Google Scholar 

  • Experian. (2013). Experian Mosaic Guide. http://www.experian.com.au/consumer-segmentation/mosaic-segments.html. Accessed April 21, 2016.

  • Government of South Australia (2011). Eyre Peninsula demand and supply statement. Adelaide: Department for Water, Government of South Australia.

    Google Scholar 

  • Government of South Australia (2013). Tonsely: Site Strategies. https://tonsley.com.au/content/uploads/2016/04/Tonsley-Site-Strategies.pdf. Accessed November 2, 2016.

  • Holyoak, N. (2003). The formulation and development of a policy sensitive analysis tool for the evaluation of travel demand management measures. Ph.D. Thesis.

    Google Scholar 

  • Holyoak, N., Taylor, M. A. P, Hadjikakou, M., Percy, S., Iankov, I., & He, H. (2016). Integrated energy transport waste and water demand forecasting and scenario planning for precincts: Final report. Research Program 2, CRC for Low Carbon Living (In Press).

    Google Scholar 

  • Holyoak, N., Taylor, M. A. P., Oxlad, L., & Gregory, J. (2005). Development of a new strategic transport planning model for Adelaide. In 28th Australasian Transport Research Forum (ATRF), Sydney: Australia, CD-ROM.

    Google Scholar 

  • Iankov, I. (2016). Greenhouse gas emission rates for traffic loads applicable to Australian roads. Ph.D. Thesis.

    Google Scholar 

  • House-Peters, L. A., & Chang, H. (2011). Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resources Research, 47, 15.

    CrossRef  Google Scholar 

  • Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building Environment, 45(7), 1683–1697.

    CrossRef  Google Scholar 

  • Marchi, A., Dandy, G., & Maier, H. (2014). Financial costs, energy consumption and greenhouse gas emissions for major supply water sources and demand management options for metropolitan Adelaide. Technical Report Series No. 14/12, Adelaide: Goyder Institute for Water Research.

    Google Scholar 

  • Newton, P., Marchant, D., Mitchell, J., Plume, J., Seo, S., & Roggema, R. (2013). Design performance assessment of urban precincts from a carbon, sustainability and resilience perspective: a scoping study Version 3.0 Exposure Draft. Research Project RP2001: Scoping study for precinct design and assessment tools, Research Program 2, CRC for Low Carbon Living.

    Google Scholar 

  • NSW BTS (2011). Sydney strategic travel model modelling future travel patterns: Technical documentation. Sydney: NSW Government.

    Google Scholar 

  • Percy, S., Aldeen, M., & Berry, A. (2015). Residential precinct demand forecasting using optimised solar generation and battery storage. In IEEE PES Asia-Pacific Power and Energy Engineering Conference. Brisbane: Australia.

    Google Scholar 

  • Polebitski, A., & Palmer, R. (2010). Seasonal residential water demand forecasting for census tracts. Journal of Water Resource Planning and Management, 136(1), 27–36.

    CrossRef  Google Scholar 

  • Ren, Z., Foliente, G., Chan, W. Y., Chen, D., Ambrose, M., & Paevere, P. (2013). A model for predicting household end-use energy consumption and greenhouse gas emissions in Australia. International Journal of Sustainable Building Technology and Urban Development, 4(3), 210–228.

    CrossRef  Google Scholar 

  • Sourghali, V., & Pugh, A. (2016). The vision and the journey for predictive modelling. In Australia’s International Water Conference & Exhibition. Melbourne: Australia.

    Google Scholar 

  • Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240.

    CrossRef  Google Scholar 

  • Sydney Water (2016). Benchmarks for Water Use. https://www.sydneywater.com.au/SW/your-business/managing-your-water-use/benchmarks-for-water-use/index.htm. Accessed August 3, 2016.

  • Torriti, J. (2014). A review of time use models of residential electricity demand. Renewable and Sustainable Energy Reviews, 37, 265–272.

    CrossRef  Google Scholar 

  • US Energy Information Administration (2016). Commercial Buildings Energy Consumption Survey: 2012 Energy Usage Summary. http://www.eia.gov/consumption/commercial/reports/2012/energyusage/. Accessed June 9, 2016.

  • Zero Waste, S. A. (2012). Optimum compaction rate for Kerbside Recyclables, Zero Waste SA and local government research and development scheme. Adelaide: Zero Waste SA.

    Google Scholar 

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Correspondence to Nicholas Holyoak .

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Holyoak, N., Taylor, M., Hadjikakou, M., Percy, S. (2017). An Integrated Demand and Carbon Impact Forecasting Approach for Residential Precincts. In: Geertman, S., Allan, A., Pettit, C., Stillwell, J. (eds) Planning Support Science for Smarter Urban Futures. CUPUM 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-57819-4_17

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