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Correlation between air temperature and electricitydemand by linear regression and wavelet coherence approach: UK, Slovakia and Bosnia and Herzegovina case study

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the results of correlations between air temperature and electricity demand by linear regression and Wavelet Coherence (WTC) approach for three different European countries are presented. The results show a very close relationship between air temperature and electricity demand for the selected power systems, however, the WTC approach presents interesting dynamics of correlations between air temperature and electricity demand at different time-frequency space and provide useful information for a more complete understanding of the related consumption.
Rocznik
Strony
521--532
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
  • EPC Elektroprivreda B&H D.D. Sarajevo, Department of Strategic Development Vilsonovo setaliste 15, 71000 Sarajevo, Bosnia and Herzegovina, s.avdakovic@elektroprivreda.ba
autor
  • EPC Elektroprivreda B&H D.D. Sarajevo, Department of Strategic Development Vilsonovo setaliste 15, 71000 Sarajevo, Bosnia and Herzegovina, al.ademovic@elektroprivreda.ba
autor
  • University of Tuzla, Faculty of Electrical Engineering, Department of Power Systems Analysis Franjevacka 2, 75000 Tuzla, Bosnia and Herzegovina, amir.nuhanovic@untz.ba
Bibliografia
  • [1] Henley, A., Peirson, J., Non-Linearities in electricity demand and temperature: Parametric Versus Non-Parametric Methods, Oxford Bulletin of Economics and Statistics 59: 149-162 (1997).
  • [2] Lee C.C., Chiu Y.B., Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach. Energy Economics 33: 896-902 (2011).
  • [3] Parkpoom S., Harrison G.P., Bialek J.W., Climate change impacts on electricity demand, Proc. of the 39th UPEC: 1342-1346 (2004).
  • [4] Kervinen, T., Estimation of the temperature dependency of the Finnish electricity consumption. Helsinki University of Technology – Independent Research Project in Applied Mathematics (2008). www.sal.tkk.fi/publications/pdf-files/eker08.pdf.
  • [5] Valor E., Meneu V., Caselles V., Daily air temperature and electricity load in Spain. Journal of Applied Meteorology 40: 1413-1421 (2001).
  • [6] Pilli-Sihvola K., Aatola P., Ollikainen M., Tuomenvirta H., Climate change and electricity consumption-witnessing increasing or decreasing use and costs? Energy Policy 38: 2409-2419 (2010).
  • [7] Gupta E., Climate change and the demand for electricity: A non-linear time varying approach. Indian Statistical Institute-Delhi (2011). www.isid.ac.in/~pu/.../dec.../EshitaGupta.pdf.
  • [8] Stojanovic M.B., Bozic M.M., Stankovic M.M., Mid-term load forecasting using recursive time series prediction strategy with support vector machines. Facta Univ. Ser. Elec. Energ. 23: 287-298 (2010).
  • [9] Mamlook R., Badran O., Abdulhadi E., A fuzzy inference model for short-term load forecasting, Energy Policy 37: 1239-1248 (2009).
  • [10] Maia, C.A., Goncalves M.M., A methodology for short-term electric load forecasting based on specialized recursive digital filters, Computers & Industrial Engineering 57: 724-731 (2009).
  • [11] Avdakovic S., Nuhanovic A., Kusljugic M., Music M., Wavelet transform applications in power system dynamics, Electric Power Systems Research 83: 237-245 (2012).
  • [12] Senjyu T., Tamaki Y., Takara H., Uezato K., Next day load curve forecasting using wavelet analysis with neural network. Electric Power Components and Systems 30: 1167-1178 (2002).
  • [13] Khoa T.Q.D., Phuong L.M., Binh P.T.T., Lien N.T.H., Application of wavelet and neural network to long-term load forecasting. Proc. of the Int. Conf. on Power System Technology-POWERCON: 840-844 (2004).
  • [14] Zhang Q., Liu T., Research on mid-long term load forecasting base on wavelet neural network. roc. of the Second Int. Conf. on Computer Engineering and Applications (ICCEA): 217-220 (2010).
  • [15] Antoniadis A., Brossat X., Cugliari J., Poggi J.M., Clustering functional data using wavelets. Society 43: 1-30 (2011).
  • [16] Frunt J., Kling W.L., Ribeiro P.F., Wavelet decomposition for power balancing analysis. IEEE Trans. on Power Delivery 26: 1608-1614 (2011).
  • [17] Fung W.Y., Lam K.S., Hung W.T., Pang S.W., Lee Y.L., Impact of urban temperature on energy consumption of Hong Kong, Energy 31: 2623-2637 (2006).
  • [18] Moghaddas-Tafreshi S.M., Mahdi F., A linear regression-based study for temperature sensitivity analysis of Iran electrical load. IEEE Inter Conf on Industrial Technology 1-7 (2008).
  • [19] Avdakovic S., Nuhanovic A., Kusljugic M., Becirovic E., Turkovic E., Wavelet multiscale analyses of a power system load variance. Turkish Journal of Electrical Eng & Comp Sci, DOI: 10.3906/elk-1109-47 (2012).
  • [20] Avdakovic S., Ademovic A., Nuhanovic A., Insight into the Properties of the UK Power Consumption Using a Linear Regression and Wavelet Transform Approach. Elektrotehniški Vestnik/Electrotechnical Review 79: 278-283 (2012).
  • [21] Torrence C., Compo GP., A practical guide to wavelet analysis, Bulletin of the American Meteorological Society 79: 61-78 (1998).
  • [22] Grinsted A., Moore J.C., Jevrejeva S., Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics 11: 561-566 (2004).
  • [23] Holman I.P., Rivas-Casado M., Bloomfield J.P., Gurdak J.J., Identifying non-stationary groundwater level response to North Atlantic ocean-atmosphere teleconnection patterns using wavelet coherence, Hydrogeology Journal 19: 1269-1278 (2011).
  • [24] Furon A.C., Wagner-Riddle C., Ryan Smith C., Warland J.S., Wavelet analysis of wintertime and spring thaw CO2 and N2O fluxes from agricultural fields. Agricultural and forest meteorology 148: 1305-1317 (2008).
  • [25] Keener V.W., Feyereisen G.W., Lall U. et al. El-Niño/Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, Georgia (GA), Journal of Hydrology 381: 352-363 (2010).
  • [26] Terradellas E., Morales G., Cuxart J., Yague C., Wavelet methods: application to the study of the stable atmospheric boundary layer under non-stationary conditions. Dynamics of Atmospheres and Oceans 34: 225-244 (2001).
  • [27] Aguiar-Conraria L., Azevedo N., Soares M.J., Using wavelets to decompose the time–frequency effects of monetary policy. Physica A, 387: 2863-2878 (2008).
  • [28] Sen A.K., Zheng J., Huang Z., Dynamics of cycle-to-cycle variations in a natural gas direct-injection spark-ignition engine. Applied Energy 88: 2324-2334 (2011).
  • [29] Vacha L., Barunik J., Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics 34: 241-247 (2012).
  • [30] http://www.pol.ac.uk/home/research/waveletcoherence/
  • [31] http://www.nationalgrid.com/
  • [32] Parker D.E., Legg T.P., Folland C.K., A new daily Central England Temperature Series. 1772-1991, Int. J. Clim. 12 : 317-342 (1992). Available: www.metoffice.gov.uk/hadobs
  • [33] http://neuron.tuke.sk/competition/index.php
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9afc70d7-99e1-41a7-aeeb-109961a00760
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