ANALYSING AND FORECASTING EUROPEAN UNION ENERGY DATA

The incessantly growing demand for energy consumption and the significance of the availability of sustainable energy for achieving long term economic growth defines the importance of forecasting energy statistics. This paper analyses and forecasts actual energy consumption data for EU-27 nations using both parametric and nonparametric time series forecasting techniques. Singular Spectrum Analysis (SSA) is adopted as the nonparametric time series analysis and forecasting technique and the results from SSA are compared with ARIMA, which is a parametric forecasting technique.

[1]  Dimitrios D. Thomakos,et al.  A review on singular spectrum analysis for economic and financial time series , 2010 .

[2]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[3]  Hossein Hassani,et al.  Singular Spectrum Analysis Based on the Minimum Variance Estimator , 2010 .

[4]  Erkan Erdogdu Electricity Demand Analysis Using Cointegration and ARIMA Modelling: A case study of Turkey , 2007 .

[5]  Abdol S. Soofi,et al.  Predicting inflation dynamics with singular spectrum analysis , 2013 .

[6]  Vitor Hugo Ferreira,et al.  Input space to neural network based load forecasters , 2008 .

[7]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[8]  René Jursa,et al.  Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models , 2008 .

[9]  Anatoly Zhigljavsky,et al.  Multivariate singular spectrum analysis for forecasting revisions to real-time data , 2011 .

[10]  Hossein Hassani,et al.  MULTIVARIATE SINGULAR SPECTRUM ANALYSIS: A GENERAL VIEW AND NEW VECTOR FORECASTING APPROACH , 2013 .

[11]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[12]  W. Charytoniuk,et al.  Very short-term load forecasting using artificial neural networks , 2000 .

[13]  M. Blanchard,et al.  Generation of autocorrelated wind speeds for wind energy conversion system studies , 1984 .

[14]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[15]  Kerry Patterson,et al.  A Comprehensive Causality Test Based on the Singular Spectrum Analysis , 2011 .

[16]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[17]  M. Medeiros,et al.  Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data , 2008 .

[18]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[19]  Christina Beneki,et al.  Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach , 2012 .

[20]  Zhengyuan Xu,et al.  Singular spectrum analysis based on the perturbation theory , 2011 .

[21]  Francesco Lisi,et al.  Is a random walk the best exchange rate predictor , 1997 .

[22]  Samer S. Saab,et al.  Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon , 2001 .

[23]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[24]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .

[25]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[26]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[27]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[28]  Chunhang Chen,et al.  Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study☆ , 1997 .

[29]  Xun Zhang,et al.  THE MORE THE BETTER: FORECASTING OIL PRICE WITH DECOMPOSITION-BASED VECTOR AUTOREGRESSIVE MODEL , 2013 .

[30]  Rahim Mahmoudvand,et al.  FILTERING AND DENOISING IN LINEAR REGRESSION ANALYSIS , 2010 .

[31]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[32]  Anatoly Zhigljavsky,et al.  Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis , 2013 .

[33]  David J. Pack,et al.  In defense of ARIMA modeling , 1990 .

[34]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[35]  Víctor M. Guerrero,et al.  Forecasting electricity consumption with extra-model information provided by consumers , 1998 .

[36]  S. Sanei,et al.  An adaptive singular spectrum analysis approach to murmur detection from heart sounds. , 2011, Medical engineering & physics.

[37]  Remy Cottet,et al.  Bayesian Modeling and Forecasting of Intraday Electricity Load , 2003 .

[38]  Ismael Sánchez,et al.  Adaptive combination of forecasts with application to wind energy , 2008 .

[39]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[40]  Chao-Ming Huang,et al.  Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting , 1995 .

[41]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[42]  R. Buizza,et al.  Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models , 2009, IEEE Transactions on Energy Conversion.

[43]  S. Koopman,et al.  An Hourly Periodic State Space Model for Modelling French National Electricity Load , 2007 .