Predicting and quantifying the effect of variations in long-term water demand on micro-hydropower energy recovery in water supply networks

References

• Adamowski, J., Chan, H.F., Prasher, S.O., Ozga-Zielinski, B., and Sliusarieva, A., 2012. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network and wavelet artificial neural network analysis for urban water demand forecasting in Montreal, Canada. Water Resource Research, 48, W01528. doi:10.1029/2010WR009945
   Web of Science ®Google Scholar
• Altunkaynak, A., Ozger, M., and Akmakici, M., 2005. Water consumption prediction of Istanbul city by fuzzy logic approach. Water Resource Management, 19, 641–654.
   Web of Science ®Google Scholar
• Arbues, F., Garcia-Valinas, M.A., and Martinez-Espineira, R., 2003. Estimation of residential water demand: A state-of-the-art review. Journal of Socio-Economics, 32, 81–100.
   Google Scholar
• Babel, M.S., and Shinde, V.R., 2011. Identifying prominent explanatory variables for water demand prediction using artificial neural networks: A case study of Bangkok. Water Resource Management, 25, 1653–1676.
   Web of Science ®Google Scholar
• Barua, S., Ng, A.W.M., Muthukumaran, S., Roberts, P., and Perera, B.J.C., 2016. Modeling water use in schools: A disaggregation approach. Urban Water Journal, 13 (8), 875–881. doi:10.1080/1573062X.2015.1056743
   Web of Science ®Google Scholar
• Billings, R.B., and Jones, C.V., 2008. Forecasting urban water demand. Denver, CO: American Water Works Association.
   Google Scholar
• Bougadis, J., and Adamowski, K., 2005. Short-term municipal water demand forecasting. Hydrological Processes, 19, 137–148.
   Web of Science ®Google Scholar
• Carravetta, A., Del Giudice, G., Fecarotta, O., and Ramos, H.M., 2012. Energy production in water distribution networks: A PAT design strategy. Water Resource Management, 26, 3947–3959.
   Web of Science ®Google Scholar
• Carravetta, A., Del Giudice, G., Fecarotta, O., and Ramos, H.M., 2013. Pump as turbine (PAT) design in water distribution network by system effectiveness. Water, 5, 1211–1225.
   Web of Science ®Google Scholar
• Carravetta, A., Fecarotta, O., Sinagra, M., and Tucciarelli, T., 2014. Cost-benefit analysis for hydropower production in water distribution networks by a pump as turbine. Journal of Water Resource Planning and Management, 140, 1–8.
   Web of Science ®Google Scholar
• Colombo, A. and Kleiner, Y., 2011. Energy recovery in water distribution systems using microturbines. In: Conference on Probabilistic Methodologies in Water and Wastewater Engineering, 23–27 September 2011, Toronto: National Research Council of Canada, 1–9.
   Google Scholar
• Corcoran, L., Coughlan, P., and McNabola, A., 2013. Energy recovery potential using micro hydropower in water supply networks in the UK and Ireland. Water Science and Technology, Water Supply, 13 (2), 552–560.
   Web of Science ®Google Scholar
• Donkor, E.A., Mazzuchi, T.A., Soyer, R., and Roberson, J.A., 2014. Urban water demand forecasting: Review of methods and models. Journal of Water Research, 140 (2), 146–159.
   Web of Science ®Google Scholar
• Fecarotta, O., Arico, C., Carravetta, A., Martino, R., and Ramos, H.M., 2015. Hydropower potential in water distribution networks: Pressure control by PATs. Water Resource Management, 29, 699–714.
   Web of Science ®Google Scholar
• Gallagher, J., Harris, I.M., Packwood, A.J., McNabola, A., and Williams, A.P., 2015. A strategic assessment of micro-hydropower in the UK and Irish water industry: Identifying technical and economic constraints. Renewable Energy, 81, 808–815.
   Web of Science ®Google Scholar
• Giugni, M., Fontana, N. and Ranucci, A., 2014. Optimal location of PRVs and turbines in water distribution systems. Journal of Water Resources Planning and Management. doi:10.1061/(ASCE)WR.1943-5452.0000418.
   Web of Science ®Google Scholar
• Goodchild, 2003. Modelling the impact of climate change on domestic water demand. Water and Environment Journal, 17 (1), 8–12.
   Web of Science ®Google Scholar
• Gonçalves, F., Costa, L., and Ramos, H., 2011. Best economical hybrid energy solution: Model development and case study of a WDS in Portugal. Energy Policy, 39 (6), 3361–3369.
   Web of Science ®Google Scholar
• Haque, M.M., Rahman, A., Hagare, D., and Kibria, G., 2013. A comparison of linear and nonlinear regression modelling for forecasting long term urban water demand: A case study for blue mountains water supply system in Australia. In: 6th International Conference on Water Resources and Environment Research, 3–7 June 2013. Koblenz: IHP Germany, 363–373.
   Google Scholar
• House-Peters, L. and Chang, H., 2011. Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resource Research, 47, W05401. doi:10.1029/2010WR009624
   PubMed Web of Science ®Google Scholar
• Howe, A., 2009. Renewable energy potential for the water industry. Report: SC070010/R5. Bristol: Environment Agency UK.
   Google Scholar
• Jain, A., Varshney, A.J., and Joshi, U.C., 2001. Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks. Water Resources Management, 15, 299–321.
   Web of Science ®Google Scholar
• Jain, A. and Ormsbee, L.E., 2002. Short-term water demand forecast modelling techniques – Convention methods versus AI. Journal American Water Works Association, 94, 64–72.
   Web of Science ®Google Scholar
• Khatri, K. and Vairavamoorthy, K., 2009. Water demand forecasting for the city of the future against the uncertainties and the global change pressures: Case of Birmingham. In: World Environmental and Water Resources Congress, 17–21 May 2009. Kansas City, MO: ASCE, (115).
   Google Scholar
• Matlab, 2014. Feed forward networks. Available from: http://www.mathworks.co.uk/help/nnet [Accessed 17 January 2015].
   Google Scholar
• McDonald, A., Butler, D., and Ridgewell, C., 2011. Water demand: Estimation, forecasting and management. In: D. Savic and J.K. Banyard, eds. Water Distribution Systems. London: ICE Publishing, 49–71.
   Google Scholar
• McNabola, A., Coughlan, P., and Williams, A.P., 2013. Energy recovery in the water industry: An assessment of the potential of micro-hydropower. Water and Environment Journal, 28 (2), 294–304. doi:10.1111/wej.12046
   Web of Science ®Google Scholar
• McNabola, A., Williams, A.P., Coughlan, P., Styles, D., Corcoran, L., Power, C., Gallagher, J., and Harris, I., 2014. Energy recovery in the water industry using micro-hydropower: An opportunity to improve sustainability. Water Policy, 16, 168–183.
   Web of Science ®Google Scholar
• Morgenroth, E., 2014. Projected population change and housing demand: A county level analysis, Technical report. Dublin: ESRI.
   Google Scholar
• OFWAT, 2014. The guaranteed standard system. Available from: http://www.ofwat.gov.uk/consumerissues/ [Accessed 17 January 2015].
   Google Scholar
• Ramos, H., Mello, M., and De, P., 2010. Clean power in water supply systems as a sustainable solution: from planning to practical implementation. Water Science and Technology, 10 (1), 39–49.
   Google Scholar
• Sitzenfrei, R. and Rauch, W., 2015. Optimizing small hydropower systems in water distribution systems based on long time series simulation and future scenarios. Journal of Water Resource Planning and Management, 141 (10). doi:10.1061/(ASCE)WR.1943-5452.0000537
   Web of Science ®Google Scholar
• Sitzenfrei, R. and von Leon, J., 2014. Long-time simulation of water distribution systems for the design of small hydropower systems. Renewable Energy, 72, 182–187.
   Web of Science ®Google Scholar
• Sweeney, J., 2001. Strive report: Climate change – refining the impacts for Ireland, Technical report. Maynooth: NUI Maynooth.
   Google Scholar
• Vijayalaksmi, D.P. and Jinesh Babu, K.S., 2015. Water supply system demand forecasting using adaptive neuro-fuzzy inference system. Aquatic Procedia, 4, 950–956.
   Google Scholar