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[11] E.S. Jr. Gardner, Exponential smoothing: The state of the art – Part II, International Journal of Forecasting 22(4) (2006), 637–666. [12] A. Goia, C. May, G. Fusai, Functional clustering and linear regression for peak load Forecasting, International Journal of Forecasting 26 (2010), 700-711. [13] G. Gould, A.B. Koehler, J. Keith Ord, R.D. Snyder, R.J. Hyndman, F. Vahid-Araghi, Forecasting time series with multiple seasonal patterns, Journal of Operational Research 191(1) (2008), 207–222. [14] N. Haldrup, S. Hylleberg, G. Pons, A. Sansó, Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data, Journal of Business & Economic Statistics 25 (2007), 21-32. [15] H.S. Hippert, D.W. Bunn, R.C. Souza, Large neural networks for electricity load forecasting: Are they over fitted?, International Journal of forecasting 21 (3) (2005), 425-434. [16] B.F. Hobbs, S. Jitprapaikulsarn, S. Konda, V. Chankong, K.A. Loparo, D.J. Maratukulam, Analysis of unit commitment of improved load forecasts, IEEE Transactions on Power Systems 14 (1999), 1342- 1348. [17] T. Hong and S. Fan, Probabilistic electric load forecasting: A tutorial review, International Journal of Forecasting, 32, (3),(2016), 914–938. [18] J. Moral-Carcedo, J. Vicns-Otero, Modelling the non-linear response of Spanish electricity demand to temperature variations, Energy Economics 27 (2005), 477-497. [19] A. Pardo, V. Meneu, E. Valor, Temperature and seasonality influences on Spanish electricity load, Energy Economics 24 (2002), 55-70. [20] B. Qiu, L. Yilu, C. Eng Kiat, L.J.C Lawrence, Load Shedding Controller over Local Area Network, Computer Application in Power 14(3) (2001), 38-43. [21] D.K. Ranaweera, G.G. Karady, R.G. Farmer, Economic impact analysis of load forecasting, IEEE Transactions on Power Systems 12 (1997), 1388−1392. [22] A. Riera, J. Mateu, Aproximación al volumen de turismo residencial en la Comunidad Autónoma de las Illes Balears a partir del cómputo de la carga demográfica real, Estudios Turístic 174 (2007), 59-71. [23] K. Song, S. Ha, J. Park, D. Kweon, K. Kim, Hybrid load forecasting method with analysis of temperature sensitivities, IEEE Transactions on Power Systems 21 (2006), 869–876. [24] R.G. Steadman, The assessment of sultriness. Part I: A temperature-humidity index based on human physiology and clothing science. Journal of Applied Meteorology 18 (1979), 861-873. [25] J. Taylor, An evaluation of methods for very short-term load forecasting using minute-by-minute British data, International Journal of Forecasting 24 (2008), 645-658. [26] J. Taylor, Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles, International Journal of Forecasting 26 (2010), 627-646. [27] J. Taylor, L. Menezes, P. McSharry, A comparison of univariate methods for forecasting electricity demand up to a day ahead, International Journal of Forecasting 22 (2006), 1–16. [28] J.W. Taylor, R. Buizza, Using weather ensemble predictions in electricity demand forecasting, International Journal of Forecasting 19 (2003), 57-70. [29] D. Trudnowsky, M. Donelly, E. Lightner, Power System Frequency and Stability Control Using Decentralized Intelligent Loads, IEEE PES T&D Conference and Exposition, Dallas, Texas, 2006. [30] E. Valor, V. Meneu, V. Caselles, Daily air temperature and electricity load in Spain, Journal of applied Meteorology 40 (2001), 1413-1421. M.BakhatandJ.RosselloNadal / ImprovingDailyElectricityLoadsForecasting 75
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Intelligent Environments 2019 Workshop Proceedings of the 15th International Conference on Intelligent Environments
Title
Intelligent Environments 2019
Subtitle
Workshop Proceedings of the 15th International Conference on Intelligent Environments
Authors
Andrés Muñoz
Sofia Ouhbi
Wolfgang Minker
Loubna Echabbi
Miguel Navarro-Cía
Publisher
IOS Press BV
Date
2019
Language
German
License
CC BY-NC 4.0
ISBN
978-1-61499-983-6
Size
16.0 x 24.0 cm
Pages
416
Category
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