Water End-Use Classification with Contemporaneous Water-Energy Data and Deep Learning Network
‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.
[1] Klein, G., Krebs, M., Hall, V., O’Brien, T. & Blevins, B. B., (2005). California’s water– energy relationship. Final Staff Report. California Energy Commission.
[2] Nguyen, K. A., Stewart, R. A., Zhang, H., & Jones, C. (2015). Intelligent autonomous system for residential water end use classification: Autoflow. Applied Soft Computing, 31, 118-131.
[3] Makki, A. A., Stewart, R. A., Beal, C. D., & Panuwatwanich, K. (2015). Novel bottom-up urban water demand forecasting model: Revealing the determinants, drivers and predictors of residential indoor end-use consumption. Resources, Conservation and Recycling, 95, 15-37.
[4] Stewart, R., Giurco, D., & Beal, C. (2013). Age of intelligent metering and big data: Hydroinformatics challenges and opportunities. Journal of the International Association for Hydro-environment Engineering and Research, 2, 107-110.
[5] Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225.
[6] Kenway, S. J., Binks, A., Lane, J., Lant, P. A., Lam, K. L., & Simms, A. (2015). A systemic framework and analysis of urban water energy. Environmental Modelling and Software, 73, 272-285.
[7] Arpke, A., & Hutzler, N. (2006). Domestic Water Use in the United States. A Life-Cycle Approach. Journal of Industrial Ecology, 10(1-2), 169-183.
[8] Cheng, C. L. (2002). Study of the inter-relationship between water use and energy conservation for a building. Energy and Buildings, 34(3), 261-266.
[9] Stokes, J.R., & Horvath, A. (2009). Energy and Air Emissions Impacts of Water Supply. Environmental Science and Technology, 43(8), 2680–2687J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility (Periodical style),” WASET Trans. Electron Devices, vol. ED-11, pp. 34–39, Jan. 1959.
[10] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2011). Application of dynamic time warping algorithm in prototype selection for the disaggregation of domestic water flow data into end use events. Proceeding of the 34th World Congress of the International Association for Hydro-Environment Engineering and Research, Brisbane, Australia.
[11] Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A (2010) Stacked denosing Autoencoders: Learning Representations in a Deep Network with Local Denoising Criterion. Journal of Machine Learning Research, 11, 3371-3408.
[12] Weston, J., Ratle, F., and Collobert, R. Deep learning via semi-supervised embedding. In William W. Cohen, Andrew McCallum, and Sam T. Roweis, editors, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML’08), pages 1168–1175, New York, NY, USA, 2008. ACM. ISBN 978-1-60558-205-4. doi: 10.1145/1390156.1390303.
[13] Chapelle, O., Scholkopf, B., and Zien, A. editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
[14] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2013a). Intelligent pattern recognition model to automate the categorisation of residential water end-use events. Journal of Environmental Modelling and Software, 47, 108-127.
[15] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2013b). Development of an intelligent model to categorise residential water end use events. Journal of Hydro-environment Research, 7(3), 182-201.
[1] Klein, G., Krebs, M., Hall, V., O’Brien, T. & Blevins, B. B., (2005). California’s water– energy relationship. Final Staff Report. California Energy Commission.
[2] Nguyen, K. A., Stewart, R. A., Zhang, H., & Jones, C. (2015). Intelligent autonomous system for residential water end use classification: Autoflow. Applied Soft Computing, 31, 118-131.
[3] Makki, A. A., Stewart, R. A., Beal, C. D., & Panuwatwanich, K. (2015). Novel bottom-up urban water demand forecasting model: Revealing the determinants, drivers and predictors of residential indoor end-use consumption. Resources, Conservation and Recycling, 95, 15-37.
[4] Stewart, R., Giurco, D., & Beal, C. (2013). Age of intelligent metering and big data: Hydroinformatics challenges and opportunities. Journal of the International Association for Hydro-environment Engineering and Research, 2, 107-110.
[5] Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225.
[6] Kenway, S. J., Binks, A., Lane, J., Lant, P. A., Lam, K. L., & Simms, A. (2015). A systemic framework and analysis of urban water energy. Environmental Modelling and Software, 73, 272-285.
[7] Arpke, A., & Hutzler, N. (2006). Domestic Water Use in the United States. A Life-Cycle Approach. Journal of Industrial Ecology, 10(1-2), 169-183.
[8] Cheng, C. L. (2002). Study of the inter-relationship between water use and energy conservation for a building. Energy and Buildings, 34(3), 261-266.
[9] Stokes, J.R., & Horvath, A. (2009). Energy and Air Emissions Impacts of Water Supply. Environmental Science and Technology, 43(8), 2680–2687J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility (Periodical style),” WASET Trans. Electron Devices, vol. ED-11, pp. 34–39, Jan. 1959.
[10] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2011). Application of dynamic time warping algorithm in prototype selection for the disaggregation of domestic water flow data into end use events. Proceeding of the 34th World Congress of the International Association for Hydro-Environment Engineering and Research, Brisbane, Australia.
[11] Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A (2010) Stacked denosing Autoencoders: Learning Representations in a Deep Network with Local Denoising Criterion. Journal of Machine Learning Research, 11, 3371-3408.
[12] Weston, J., Ratle, F., and Collobert, R. Deep learning via semi-supervised embedding. In William W. Cohen, Andrew McCallum, and Sam T. Roweis, editors, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML’08), pages 1168–1175, New York, NY, USA, 2008. ACM. ISBN 978-1-60558-205-4. doi: 10.1145/1390156.1390303.
[13] Chapelle, O., Scholkopf, B., and Zien, A. editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
[14] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2013a). Intelligent pattern recognition model to automate the categorisation of residential water end-use events. Journal of Environmental Modelling and Software, 47, 108-127.
[15] Nguyen, K. A., Zhang, H., & Stewart, R. A. (2013b). Development of an intelligent model to categorise residential water end use events. Journal of Hydro-environment Research, 7(3), 182-201.
@article{"International Journal of Information, Control and Computer Sciences:76732", author = "Khoi A. Nguyen and Rodney A. Stewart and Hong Zhang", title = "Water End-Use Classification with Contemporaneous Water-Energy Data and Deep Learning Network", abstract = "‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.
", keywords = "Deep learning network, smart metering, water end use, water-energy data.", volume = "12", number = "1", pages = "1-6", }
