AIML-Driven Energy Forecasting via Roosters Optimization
Keywords:
Renewable Energy Forecasting, BLSTM, GRU, Roosters Optimization Algorithm, Deep Learning, Time-Series Analysis, Smart Grid, Feature Selection, Energy Prediction.Abstract
Renewable energy forecasting plays an important role in modern energy systems, especiallybecause renewable sources like solar and wind are highly variable and weather-dependent.Predicting how much energy can be generated from these sources is essential for planning,balancing power grids, and reducing reliance on fossil fuels.
References
Ahmed, R., & Khalid, M. (2019). A review on the selected applications of forecasting models in renewable energy systems. Renewable and Sustainable Energy Reviews, 100, 9–21.
Khosravi, A., Nahavandi, S., Creighton, D., & Atiya, A. F. (2015). Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks and Learning Systems, 22(9), 1341–1356.
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Published
2023-12-12
How to Cite
Narendra Chennupati. (2023). AIML-Driven Energy Forecasting via Roosters Optimization. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1243–1250. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2548
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