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@article{183210,
        author = {D. R. Solanke},
        title = {Forecasting of Wind Energy Resources using Support Vector Machine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {642-647},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183210},
        abstract = {The depletion of conventional fossil fuels, driven by the growing global population, underscores the need for alternative energy sources. Renewable energies, particularly wind, have proven to be viable substitutes that can meet global energy demands while preserving the environment. Accurate forecasting of wind energy resources is crucial for their optimal utilization, with various methods applied to achieve this. Among these, the support vector machine (SVM) modelling approach has demonstrated superior prediction performance, offering speed, simplicity, reliability, and accuracy. Critical analysis reveals that hybrid SVM models can achieve even higher accuracy for predicting wind energy across different locations. This investigation identifies key challenges and opportunities in this field and proposes novel hybrid models for future research to enhance the precision of wind energy predictions.},
        keywords = {Artificial Neural Network, Forecasting, Support Vector Machine, Wind Energy.},
        month = {August},
        }