Wind Power Prediction Based on Artificial Neural Network Methods

Wind power prediction represents a critical research direction in renewable energy, where artificial neural networks (ANNs) have gained widespread application due to their powerful nonlinear fitting capabilities. ANN models implemented in MATLAB can effectively handle the time-series characteristics of wind power data to forecast power generation for periods extending several dozen hours ahead. The prediction workflow primarily consists of three main stages: data preprocessing, network construction, and training optimization. In the data preprocessing phase, historical wind power data undergoes cleaning and normalization to eliminate dimensional differences and accelerate model convergence. Common MATLAB functions like normalize or zscore are typically employed for data standardization. Subsequently, an appropriate neural network architecture for time-series forecasting is constructed, typically utilizing multilayer perceptrons (MLP) or Long Short-Term Memory (LSTM) networks. These models take historical power data, wind speed, and other feature inputs to generate power predictions for future time points. In MATLAB implementation, the nntool or Deep Learning Toolbox provides built-in functions for creating such network structures. The network training phase focuses on hyperparameter optimization, including learning rate selection, determination of hidden layer neurons, and early stopping techniques to prevent overfitting. Functions such as trainlm (Levenberg-Marquardt) or trainbr (Bayesian Regularization) are commonly used training algorithms. The trained model can capture complex nonlinear relationships between wind speed and power conversion, demonstrating superior adaptability compared to traditional physical models. Prediction results can be integrated into power system dispatch operations to enhance grid stability for wind power integration. This approach offers significant advantages by eliminating the need for precise physical modeling, instead employing data-driven methods to automatically learn underlying patterns. Future enhancements could incorporate advanced algorithms like attention mechanisms to further improve long-term prediction accuracy. MATLAB's deep learning frameworks provide excellent support for implementing these advanced architectures through functions like attentionLayer in the Deep Learning Toolbox.