EFFECT OF TIME HORIZON ON WIND SPEED PREDICTION WITH ANN

Proper utilization of renewable energy sources in electricity production is inevitable due to the environmental concerns and global warming fight. Therefore, predictability of renewable electricity is a very significant issue for a long time. Main aim of this study, different from the literature, is to investigate the change of wind speed prediction errors for different time horizons. Different prediction time horizons (10, 30, 60, 90 and 120 minutes) were used, and the results were compared through the error measures and the regression values. The mean squared errors and the regression values vary between 0.819 and 5.570, and between 77.8% and 97.1%, respectively. The prediction error changes almost logarithmically, and the rate of change decreases with the increasing time horizon. A new analysis approach was proposed to see the change of the prediction error with time horizon. The equation, y = 1.5413 ln ( x ) - 2.7428, representing the change of the mean squared error with time horizon was obtained.

[1]  M. Bilgili,et al.  Application of artificial neural networks for the wind speed prediction of target station using reference stations data , 2007 .

[2]  Ali Heidari,et al.  Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory , 2014, Comput. Intell. Neurosci..

[3]  M. Milligan,et al.  A Comparison and Case Study of Capacity Credit Algorithms for Intermittent Generators , 1997 .

[4]  Bent Sørensen,et al.  A sustainable energy future: Construction of demand and renewable energy supply scenarios , 2008 .

[5]  Coşkun Özkan,et al.  Prediction of Accurate Values for Outliers in Coal Drying Experiments , 2015 .

[6]  Şaban Pusat.  Study to determine wind energy potential for Sakarya University , 2017 .

[7]  H. Nogay,et al.  Wind Speed Correlation Between Neighboring Measuring Stations , 2012 .

[8]  Gerd Tetzlaff,et al.  Wind Speed Forecasting from 1 to 30 Minutes , 1998 .

[9]  Serhan Ozdemir,et al.  Wind speed time series characterization by Hilbert transform , 2006 .

[10]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[11]  Pierre Pinson,et al.  On‐line assessment of prediction risk for wind power production forecasts , 2003 .

[12]  Pengfei Li,et al.  A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data , 2014, Comput. Intell. Neurosci..

[13]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

[14]  Tolga Taner,et al.  Energy and Economic Analysis of the Wind Turbine Plant’s Draft for the Aksaray City , 2014 .

[15]  C. Coskun,et al.  Estimation of the Monthly Based Hourly Wind Speed Characteristics and the Generated Power Characteristics for Developing Bidding Strategies in an Actual Wind Farm: A Case Study , 2013 .

[16]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[17]  Sanjoy Roy,et al.  Economic assessment of the engineering basis for wind power: Perspective of a vertically integrated utility , 2009 .

[18]  Luis A. Fernández-Jiménez,et al.  An Advanced Model for Short-Term Forecasting of Mean Wind Speed and Wind Electric Power , 2004, Control. Intell. Syst..

[19]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

[20]  Y. D. Song.  A New Approach for Wind Speed Prediction , 2000 .

[21]  L. Liu,et al.  A modeling approach for investigating climate change impacts on renewable energy utilization , 2012 .

[22]  Gerhard-Wilhelm Weber,et al.  CMARS and GAM & CQP - Modern optimization methods applied to international credit default prediction , 2011, J. Comput. Appl. Math..

[23]  X. Wang,et al.  Wind speed forecasting for power system operational planning , 2005, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[24]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[25]  Amy Sopinka,et al.  The Economics of Storage, Transmission and Drought: Integrating Variable Wind Power into Spatially Separated Electricity Grids , 2012 .

[26]  R. E. Abdel-Aal,et al.  Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks , 2009 .

[27]  Pavlos S. Georgilakis,et al.  Technical challenges associated with the integration of wind power into power systems , 2008 .

[28]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[29]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[30]  Pietro Vecchio,et al.  Wind energy prediction using a two-hidden layer neural network , 2010 .

[31]  S. Rehman,et al.  Spatial estimation of wind speed , 2012 .

[32]  D. Swift-hook.  The case for renewables apart from global warming , 2013 .

[33]  Gerhard-Wilhelm Weber,et al.  RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty , 2014, J. Comput. Appl. Math..

[34]  Coşkun Özkan,et al.  Estimation of coal moisture content in convective drying process using ANFIS , 2016 .

[35]  W. Rivera,et al.  Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks , 2009 .

[36]  E.F. El-Saadany,et al.  One day ahead prediction of wind speed using annual trends , 2006, 2006 IEEE Power Engineering Society General Meeting.

[37]  Athanasios Sfetsos,et al.  A novel approach for the forecasting of mean hourly wind speed time series , 2002 .