Abstract
The concept of demand profiling is established in order to collect, analyse and develop the detailed knowledge of the consumption habits, either in domestic or non-domestic usage. In this paper the state representation of electrical signal is used as the profiling formula to model the diurnal (daily) and annual cycle demand trend of electricity consumption across the grid. The available demand dataset from the public domain is applied as the input for the profiling formula. The developed demand profile is further to be forecast and assimilated using the active-aware-based Ensemble Kalman Filter (EnKF). The resultant EnKF estimations may provide the assessment of nationwide demand within the energy network, thus consider the need for the present and future network reinforcement or upgrades. The ability of EnKF in forecasting the demand is presented, along with the limitations.
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References
E.ON UK. The energy trilemma (2016). https://www.eonenergy.com/for-your-business/large-energy-users/manage-energy/energy-efficiency/decentralised-energy-experts/The-energy-trilemma. Accessed 17 Feb 2016
Lau, E.T.: Quantification of carbon emissions and savings in smart grids. Phd thesis, College of Engineering, Design and Physical Sciences, Brunel University London (2016)
Plat, R., Williams, J., Pardoe, A., Straw, W.: A new approach to electricity markets - how new, distruptive technologies change everything. Technical report, Institute for Public Policy Research (2014)
Elexon. Load profiles and their use in electricity settlement (2013). https://www.elexon.co.uk/wp-content/uploads/2013/11/load_profiles_v2.0_cgi.pdf. Accessed 30 Apr 2016
DoE. Module 5: Energy assessment - demand analysis (2011). http://www.energy.gov.za/EEE/Projects/Building%20Energy%20Audit%20Training/Training%20Modules/Building%20Energy%20Auditing%20Module%205_final:pdf. Accessed 30 Apr 16
Energy Efficiency Exchange. Understanding your energy requirements (2016). http://eex.gov.au/energy-management/energy-procurement/procuring-and-managing-energy/understanding-your-energy-requirements/. Accessed 30 Apr 2016
Lau, E.T., Yang, Q., Forbes, A.B., Wright, P., Livina, V.N.: Modelling carbon emissions in electric systems. Energy Convers. Manage. 80(59), 573–581 (2014)
National Grid. Data explorer (2016). http://www2.nationalgrid.com/UK/Industry-information/Electricity-transmission-operational-data/Data-Explorer/. Accessed 01 May 2016
Sumer, K.K., Goktas, O., Hepsag, A.: The application of seasonal latent variable in forecasting electricity demand as an alternative method. Energy Policy 37(4), 1317–1322 (2009)
Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160(2), 501–514 (2005)
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural network for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16, 44–55 (2002)
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. Geophys. Res. 99(5), 10143–10162 (1994)
John, C.J., Mandel, J.: A two-stage Ensemble Kalman Filter for smooth data assimilation. Environ. Ecol. Stat. 15, 101–110 (2008)
Almendral-Vazquez, R., Syversveen, A.R.: The Ensemble Kalman Filter - theory and applications in oil industry. Technical report, Norsk Regnesentral (2006). https://www.nr.no/en/nrpublication?query=/file/4334/Almendral_Vazquez_-_Ensemble_Kalman_Filter_-_theory_and_applications_i.pdf. Accessed 25 Jul 2015
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dyn. 53(4), 343–367 (2003)
Gillijins, S., Barrero Mendoza, O.B., Chandrasekar, J., De Moor, B.L.R., Bernstein, D.S., Ridley, A.: What is the Ensemble Kalman Filter and how well does it work? In: Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, USA, pp. 4448–4453. IEEE (2006)
Jensen, J.P.: Ensemble Kalman Filtering for state and parameter estimation on a reservoir model. Master thesis, Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim (2007)
Nævdal, G., Johnsen, L.M., Aanonsen, S.I., Vefring, E.H.: Reservoir monitoring and continuous model updating using Ensemble Kalman Filter. In: SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, pp. 1–12. SPE (2003)
Anderson, J.L.: Localization and sampling error correction in Ensemble Kalman Filter data assimilation. Am. Meteorol. Soc. 140, 2359–2371 (2012)
Elexon. Profiling - Average profiling data per Profile Class (regression data evaluated at 10-year average temperatures (2016). https://www.elexon.co.uk/reference/technical-operations/profiling/. Accessed 30 Apr 2016
DECC. Sub-national electricity and gas consumption statistics: analysis tool 2005 to 2014 (2015). https://www.gov.uk/government/publications/sub-national-electricity-and-gas-consumption-statistics-analysis-tool-2005-to-2009. Accessed 22 Mar 2016
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Lau, E.T., Chai, K.K., Chen, Y. (2017). Demand Profiling and Demand Forecast Using the Active-Aware-Based Ensemble Kalman Filter. In: Lau, E., et al. Smart Grid Inspired Future Technologies. SmartGift 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-319-61813-5_11
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DOI: https://doi.org/10.1007/978-3-319-61813-5_11
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