Offline Prediction for a Wind Farm in Zhangjiakou

In this document, we will discuss in detail the offline prediction for a wind farm located in Zhangjiakou. This wind farm represents a significant energy project, and to enhance the accuracy of its power generation forecasts, we employ a core algorithm: neural networks. Neural networks are advanced computational models that learn patterns from historical data through training processes to predict wind farm output. The implementation typically involves data preprocessing (handling missing values, normalization), defining the network architecture (e.g., layers, activation functions like ReLU), and optimizing parameters via backpropagation. Key functions may include `trainNetwork` for model training and `predict` for generating forecasts. By leveraging neural networks, we achieve more precise predictions of the wind farm's power generation, enabling improved energy planning and management. The offline prediction task is critical as it helps analyze operational patterns and supports measures to enhance generation efficiency. In the following sections, we will elaborate on this offline prediction project, including the principles and applications of the neural network algorithm, along with code-related implementation strategies.