{"id":1189065,"date":"2026-10-07T13:55:12","date_gmt":"2026-10-07T20:55:12","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/nowcasting-of-wind-energy-with-lstm-bilstm-and-auto-regressive-techniques\/"},"modified":"2026-10-16T16:21:38","modified_gmt":"2026-10-16T23:21:38","slug":"nowcasting-of-wind-energy-with-lstm-bilstm-and-auto-regressive-techniques","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/nowcasting-of-wind-energy-with-lstm-bilstm-and-auto-regressive-techniques\/","title":{"rendered":"Nowcasting of Wind Energy with LSTM, BiLSTM, and Auto-Regressive Techniques"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Accurate prediction of wind energy potential is crucial for optimising renewable energy investments and ensuring efficient power generation. This study addresses the challenge of wind energy forecasting using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Auto-Regressive (AR) models on two real-world wind energy datasets with hourly and 15-minute resolutions. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination <math><mrow><mo stretchy=\"true\">(<\/mo><msup><mi>R<\/mi><mn>2<\/mn><\/msup><mo stretchy=\"true\">)<\/mo><\/mrow><\/math>. Experimental results show that for Dataset-1, LSTM achieves superior long-term forecasting accuracy with an <math><mrow><msup><mi>R<\/mi><mn>2<\/mn><\/msup><\/mrow><\/math> of 0.9749 for one-week prediction and maintains robustness for six-month forecasts <math><mrow><mo stretchy=\"false\">(<\/mo><msup><mi>R<\/mi><mn>2<\/mn><\/msup><mo>=<\/mo><mn>0.9576<\/mn><mo stretchy=\"false\">)<\/mo><\/mrow><\/math>, outperforming both Bil-STM and AR models. BiLSTM demonstrates marginally better short-term performance, achieving the highest <math><mrow><msup><mi>R<\/mi><mn>2<\/mn><\/msup><\/mrow><\/math> of 0.8856 for 24-hour prediction. For Dataset-2, AR exhibits strong short-term performance with an <math><mrow><msup><mi>R<\/mi><mn>2<\/mn><\/msup><\/mrow><\/math> of 0.9780 and MAPE of 2.17% for 24-hour forecasting, while LSTM and BiLSTM dominate mediumand long-term horizons, consistently achieving <math><mrow><msup><mi>R<\/mi><mn>2<\/mn><\/msup><\/mrow><\/math> values above 0.99. These findings highlight the trade-offs between model complexity, accuracy, and computational efficiency, providing practical insights for selecting appropriate forecasting models across different wind energy prediction horizons.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Accurate prediction of wind energy potential is crucial for optimising renewable energy investments and ensuring efficient power generation. This study addresses the challenge of wind energy forecasting using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Auto-Regressive (AR) models on two real-world wind energy datasets with hourly and 15-minute resolutions. Model performance is evaluated using [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"S. S","user_id":0},{"type":"text","value":"B. 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