Wind Speed Prediction Using Artificial Neural Networks with Multi-Factor Input Data

This project implements a wind speed prediction system using Artificial Neural Networks (ANN) with multi-variable input data. The ANN model architecture typically includes input layers for meteorological parameters (temperature, humidity, air pressure), hidden layers with activation functions like ReLU or sigmoid, and an output layer for wind speed regression prediction. Key implementation aspects involve data normalization using Min-Max scaling or Z-score standardization, backpropagation algorithms for weight optimization, and evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for model accuracy assessment. The core algorithm employs supervised learning where historical weather data trains the network to establish nonlinear relationships between input variables and wind speed outcomes. Feature engineering techniques may include time-series lag features and rolling averages to capture temporal patterns. The project systematically analyzes correlation coefficients between input factors and prediction outputs using Pearson correlation matrices and partial dependence plots. Model validation incorporates k-fold cross-validation to prevent overfitting, while hyperparameter tuning optimizes network depth, neuron count, and learning rates. Practical applications span renewable energy optimization for wind turbine efficiency forecasting and meteorological services for severe weather alerts. The implementation provides actionable insights into ANN's predictive capabilities for atmospheric phenomena, demonstrating potential benefits in energy management and climate research through Python frameworks like TensorFlow/Keras or MATLAB's Neural Network Toolbox.