Forecasting renewable energy for environmental resilience through computational intelligence

Abstract

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

Optimized ensemble model for wind power forecasting using hybrid whale and dipper-throated optimization algorithms

“…Using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTMs) with fine-tuned parameters, the power output of offshore wind turbines may be accurately forecasted. Reduced computation time and enhanced prediction accuracy resulted from optimizing the LSTM with adaptive differential evolution and the sines and cosines selection approach (Khan et al, 2021;Khafaga et al, 2022).…”

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

“…Refs. [29,31,[128][129][130][131][132][133][134] mainly used machine learning, deep learning, wavelet transform, time-series analysis, and other methods to predict wind speed, wind power, wave height, and wave period, and to design optimal maintenance strategies. These studies have been empirically validated in different sea areas and time frames, and the results show that they outperform other traditional models.…”