Forecasting renewable energy for environmental resilience through computational intelligence.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy
• Environmental Engineering

Background:

• Accurate wind power forecasting is crucial for efficient wind power generation and grid integration.
• Offshore wind power forecasting presents unique challenges due to harsh operational environments.
• Enhancing environmental resilience in energy systems necessitates reliable renewable energy predictions.

Purpose of the Study:

• To develop and validate a robust deep learning model for offshore wind power forecasting.
• To improve the accuracy and reliability of wind energy predictions in complex offshore conditions.
• To contribute to the design of resilient energy transition pathways through advanced forecasting.

Main Methods:

• Data preprocessing including fragmentation filtering and high-dimensional feature selection using Deep Auto-Encoding.
• Hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models for prominent wind feature training.
• Fine-tuning of the CNN-LSTM deep learning hybrid model with various parameters for offshore wind farms.

Main Results:

• The proposed CNN-LSTM model demonstrated superior performance in offshore wind power forecasting.
• Achieved the lowest root mean square error (RMSE) and mean absolute error (MAE) compared to existing models.
• High forecasting accuracy was maintained across three different offshore wind farms.

Conclusions:

• The CNN-LSTM hybrid deep learning strategy is highly effective for offshore wind power forecasting.
• The model's reliability supports enhanced environmental resilience and efficient energy transition.
• Findings provide a valuable tool for optimizing offshore wind energy management and grid integration.