Wind Power Prediction Using Neural Networks and Time Series Analysis

Wind power prediction represents a crucial research direction in the renewable energy field, where accurate forecasting can optimize grid dispatch and enhance the economic efficiency of wind farms. This article presents a MATLAB implementation approach that integrates neural networks with time series analysis methods for wind power prediction.

The data preprocessing phase forms the foundation of the entire prediction pipeline. This involves collecting historical meteorological data including wind speed, wind direction, temperature, along with corresponding wind power output data. These datasets require normalization to eliminate scale differences among variables. Additionally, missing value imputation and outlier treatment are essential to ensure data quality - typical MATLAB functions like normalize(), fillmissing(), and rmoutliers() can be employed for these operations.

The time series analysis component primarily utilizes Autoregressive Moving Average (ARMA) or Seasonal Autoregressive Integrated Moving Average (SARIMA) models. These models effectively capture temporal dependencies and seasonal patterns in wind power data. By analyzing autocorrelation and partial autocorrelation functions (using acf() and pacf() in MATLAB), optimal model parameters (p, d, q for ARMA; P, D, Q, S for SARIMA) can be determined through model identification techniques.

Neural network model construction serves as the core prediction component. Long Short-Term Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN), are typically employed due to their ability to handle long-term dependencies in time series data. The network architecture consists of input, hidden, and output layers, with LSTM units comprising the hidden layer. Input features may include multiple variables such as historical power data, wind speed, and temperature. In MATLAB, this can be implemented using the lstmLayer() function within a layerGraph structure.

During model training, the dataset should be partitioned into training, validation, and test sets. The Adam optimizer is commonly used for parameter updates, with Mean Squared Error (MSE) serving as the loss function. To prevent overfitting, techniques like dropout layers (using dropoutLayer()) and L2 regularization can be incorporated. Training progress should be monitored via validation set performance, with early stopping strategies implemented when validation loss plateaus.

In the prediction evaluation phase, multiple metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) need to be calculated. Comparative curves between actual and predicted power values can be plotted to visually demonstrate forecasting performance. For wind power prediction, particular attention should be paid to forecasting accuracy during high-power periods, as this is critical for grid dispatch operations. MATLAB's perfcurve() and plot functions facilitate this evaluation process.

In practical applications, this prediction system can be deployed as a real-time forecasting platform, incorporating latest meteorological forecast data for rolling predictions. The forecasting results assist grid operators in developing dispatch plans in advance, thereby increasing wind power integration rates and reducing wind curtailment phenomena. The system can be implemented as a MATLAB App or deployed using MATLAB Compiler for operational use.