Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering

In this paper a spatio-temporal analysis of wind power resource in the Iberian Peninsula is presented. The study uses the Second-Order Data-Coupled Clustering (SODCC) algorithm over reanalysis data in the for the period 1979 – 2014. Several characteristics of the method are detailed, such as the data-coupled clustering approach of SODCC, that ensures the non-singularity of the signal subspace within each cluster. The performance of the proposed approach and specific results obtained have been discussed in a case study in the Iberian Peninsula. In these results it is possible to identify different spatio-temporal patterns of the wind data statistics depending on the initialization year. Moreover, this work also shows that there is a close relationship between these spatio-temporal patterns with the wind energy production of the area under study, so the proposed analysis can be extended to wind farms efficiency production at the time scales considered.

[1]  Dong Jiang,et al.  Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data , 2013 .

[2]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[3]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[4]  Aliashim Albani,et al.  Spatial Analysis of Wind Potential for Malaysia , 2015, International Journal of Renewable Energy Research.

[5]  Shahaboddin Shamshirband,et al.  Comparative study of clustering methods for wake effect analysis in wind farm , 2016 .

[6]  Shuang Gao,et al.  Wind power day-ahead prediction with cluster analysis of NWP , 2016 .

[7]  Robert J. Davy,et al.  Long-Term Wind Speed Trends over Australia , 2012 .

[8]  Eduardo Morgado,et al.  Scalable Data-Coupled Clustering for Large Scale WSN , 2015, IEEE Transactions on Wireless Communications.

[9]  Wang Jilong,et al.  Short-term wind speed forecasting based on spectral clustering and optimised echo state networks , 2015 .

[10]  Tamás Vicsek,et al.  Dynamic Scaling for Aggregation of Clusters , 1984 .

[11]  Morteza Rahimiyan,et al.  Impact of spatio-temporal correlation of wind production on clearing outcomes of a competitive pool market , 2016 .

[12]  Shiyuan Zhong,et al.  Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis , 2015 .

[13]  Kenneth Tze Kin Teo,et al.  Wind energy assessment considering wind speed correlation in Malaysia , 2016 .

[14]  K. Kohfeld,et al.  The Impacts of Wind Speed Trends and 30-Year Variability in Relation to Hydroelectric Reservoir Inflows on Wind Power in the Pacific Northwest , 2013, PloS one.

[15]  Nikolaos S. Thomaidis,et al.  Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource , 2016 .

[16]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[17]  M. Branick.  A Climatology of Significant Winter-Type Weather Events in the Contiguous United States, 1982–94 , 1997 .

[18]  Sancho Salcedo-Sanz,et al.  Heuristic correction of wind speed mesoscale models simulations for wind farms prospecting and micrositing , 2014 .

[19]  Thomas Kailath,et al.  Fast subspace decomposition , 1994, IEEE Trans. Signal Process..

[20]  Kameshwar Poolla,et al.  Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform , 2016 .

[21]  K. M. Zishka,et al.  The Climatology of Cyclones and Anticyclones over North America and Surrounding Ocean Environs for January and July, 1950–77 , 1980 .

[22]  Andrew Kusiak,et al.  Short-term prediction of wind power with a clustering approach , 2010 .

[23]  Gianpaolo Vitale,et al.  Environmental data processing by clustering methods for energy forecast and planning , 2011 .

[24]  V. Gómez-Muñoz,et al.  Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques , 2002 .

[25]  Sancho Salcedo-Sanz,et al.  Spatio-temporal trend analysis of air temperature in Europe and Western Asia using data-coupled clustering , 2015 .