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@article{170791,
        author = {Bright Osagie Eze and Olayinka Sakiru Ayorinde},
        title = {Prediction of Renewable Energy Generation using Machine Learning a Systematic Review of Literature},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1714-1718},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170791},
        abstract = {As renewable energy sources have been integrated into power grids, forecasting energy generation has become increasingly difficult. The aim of this paper is to provide a systematic literature review on machine learning applications in predicting renewable energy generation, focusing on recent research. The purpose of this article is to review various machine learning techniques used for forecasting solar, wind, and other renewable energy sources. Specifically, the review illustrates how deep learning models, including long short-term memory networks and ensemble methods, can handle the variability and uncertainty associated with renewable energy generation. The results of our study demonstrate that machine learning approaches consistently outperform traditional forecasting methods, and that they provide both improved accuracy and reliability.},
        keywords = {Renewable Energy Generation, GRU, LSTM, Machine Learning, Deep Learning, Renewable Energy, Forecasting, Prediction, Solar Energy, Wind Energy.},
        month = {December},
        }