Wind Speed Prediction Using PSO+BP Algorithm with Data Preprocessing

In this technical documentation, we explore wind speed prediction employing the PSO+BP hybrid algorithm. The implementation begins with crucial preprocessing of wind speed datasets, which involves data cleaning operations (handling missing values and outliers) and normalization procedures to standardize data ranges - typically implemented using standardization methods in Python's sklearn.preprocessing module. Subsequently, we conduct simulation predictions using the Particle Swarm Optimization (PSO) enhanced Backpropagation Neural Network, where PSO optimizes BP neural network's initial weights and thresholds through swarm intelligence principles. The algorithm implementation typically involves defining fitness functions for PSO optimization and configuring BP network parameters through frameworks like TensorFlow or PyTorch. Furthermore, we detail the underlying principles of PSO-BP hybridization, highlighting advantages such as accelerated convergence and reduced local minima trapping. The document also covers model parameter tuning methodologies, including learning rate adjustments and swarm size optimization using grid search techniques. Through this comprehensive guide, you will learn practical approaches to enhance wind speed prediction accuracy and reliability by implementing optimized PSO-BP neural network architectures with proper data preprocessing pipelines.