Dépôt numérique

Long-term forecasting of wind speed in the UAE using nonlinear canonical correlation analysis (NLCCA).

Woldesellasse, Haile, Marpu, Prashanth R. et Ouarda, Taha B. M. J. ORCID: https://orcid.org/0000-0002-0969-063X (2020). Long-term forecasting of wind speed in the UAE using nonlinear canonical correlation analysis (NLCCA). Arabian Journal of Geosciences , vol. 13 , nº 18. DOI: 10.1007/s12517-020-05981-9.

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Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. Several linear and nonlinear multivariate techniques have been used to predict the stochastic character of wind speed. Wind speed forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective power grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study, nonlinear canonical correlation analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed of UAE. The wind dataset was obtained from six ground stations spread within the country. The first mode of NLCCA captured the nonlinear mode of the teleconnection indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the relative root mean square error (rRMSE) and relative mean absolute error (MAER). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behavior of the dataset of variables than linear canonical correlation analysis (CCA) model in terms of the correlation and root mean square error.

Type de document: Article
Mots-clés libres: NLCCA; CCA; neural network; teleconnection indices; wind speed
Centre: Centre Eau Terre Environnement
Date de dépôt: 16 mars 2021 14:58
Dernière modification: 15 févr. 2022 20:32
URI: https://espace.inrs.ca/id/eprint/11454

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