Enhancing wind power forecasting accuracy through LSTM with adaptive wind speed calibration (C-LSTM).

ABSTRACT: Wind power constitutes a pivotal component in the quest for carbon neutrality, serving as a principal renewable energy source. Enhancing the accuracy of wind power forecasting facilitates more efficient exploitation of this resource, with deep-learning models, notably Long Short-Term Memory (LSTM), proving effective in advancing forecasting capabilities within this domain. Nevertheless, the accuracy of wind power forecasting is undermined by the inaccurate forecasted wind speed, which diminish the reliability of such predictions. To address this challenge, we propose the model "LSTM with Adaptive Wind Speed Calibration (C-LSTM)", which integrates a mechanism into LSTM that autonomously calibrates forecasted wind speed during the training and inference phase. Specifically, considering the i

SUBMITTER: Wang D 

PROVIDER: S-EPMC11825933 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature