MATLAB Code Implementation for Wind Speed Prediction

Wind speed prediction is a critical task in the wind energy industry, where accurate forecasting of future wind speeds helps optimize the operational efficiency of wind power generation systems. This article introduces the methodology and implementation approaches for wind speed prediction using MATLAB.

### 1. Data Preprocessing Wind speed data typically contains noise and outliers, making preprocessing the essential first step. Common processing methods include: Data cleaning: Eliminate outliers using moving average or median filtering (implemented via MATLAB's movmean or medfilt1 functions). Normalization: Standardize data using Min-Max scaling (rescale function) or Z-Score normalization (zscore function) to improve model convergence. Temporal feature extraction: Decompose timestamps into cyclical features like hour-of-day and day-of-week using MATLAB's datetime arrays and circDist function for periodic encoding.

### 2. Model Selection Depending on requirements, choose from these classical approaches: Statistical models: ARIMA (Autoregressive Integrated Moving Average) implemented via arima and estimate functions, suitable for linear time series data. Machine learning models: Support Vector Regression (SVR) using fitrsvm function or Random Forest via TreeBagger, capable of capturing nonlinear relationships. Deep learning models: LSTM (Long Short-Term Memory) networks implemented with sequence-to-sequence architectures using lstmLayer and trainNetwork functions, ideal for hourly predictions with temporal dependencies.

### 3. Training and Validation Evaluate model performance through these steps: Dataset splitting: Partition data into 7:2:1 ratio for training, validation, and test sets using cvpartition or custom indexing. Hyperparameter optimization: Tune parameters like LSTM hidden units through cross-validation (crossval) or Bayesian optimization (bayesopt). Metric evaluation: Assess accuracy using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculated via custom functions or regression metrics.

### 4. Deployment and Prediction Trained models can perform real-time inference on new data: Sliding window input: Feed past N hours of data (e.g., 24 hours) using buffer function to predict next-hour wind speed. Post-processing: Apply inverse normalization (rescale with original min-max values) to convert outputs back to actual wind speed units.

### Extension Ideas Multivariate input: Incorporate meteorological data like temperature and pressure using multivariate time series networks (sequenceInputLayer) to enhance prediction robustness. Ensemble learning: Combine predictions from multiple models using bagging (bagEnsemble) or stacking techniques to further reduce errors.

Through this workflow, MATLAB provides an efficient end-to-end solution for wind speed prediction, delivering reliable support for wind farm operations with comprehensive code implementation examples.