Fuzzy granulation-based wind speed prediction with multi-objective optimization.

Area of Science:

• Energy Systems
• Artificial Intelligence
• Renewable Energy

Background:

• Accurate wind power forecasting is vital for integrating renewable energy and supporting decarbonization.
• The stochastic nature of wind resources presents challenges for short-to-medium-term forecasting, causing operational uncertainties.
• Existing forecasting models often struggle with consistent high accuracy, driving the need for methodological advancements.

Purpose of the Study:

• To introduce a novel multi-scale forecasting framework for improved wind power prediction.
• To address limitations in current forecasting techniques by enhancing accuracy and robustness.
• To effectively manage the trade-off between prediction accuracy and computational efficiency.

Main Methods:

• Integration of fuzzy information granulation to capture intrinsic features and reduce data complexity from volatile wind speed data.
• Utilization of a multi-objective optimization strategy with multiple neural networks employing diverse predictive principles.
• Adaptive integration of neural network outputs via heuristic optimization algorithms for enhanced prediction.

Main Results:

• The proposed framework demonstrates outstanding performance on the Penglai wind farm dataset.
• Fuzzy information granulation effectively mitigates noise interference for deep learning models.
• The collaborative optimization algorithm successfully balances prediction accuracy and computational efficiency.

Conclusions:

• The novel multi-scale forecasting framework significantly enhances wind power prediction accuracy and robustness.
• Fuzzy information granulation is effective in handling complex and volatile wind speed data.
• The developed approach offers a viable solution for improving the reliability of wind energy systems.