Wind Speed Prediction using Interpretable Stacking Ensemble Machine Learning Algorithms

Introduction

The domain of machine learning is rapidly advancing and has changed the way we process and analyze information. By leveraging complex algorithms and statistical models, machine learning enables computers to learn from data and make predictions or decisions based on that learning.

The aim of this study is to improve wind energy predictions using the Stacking Ensemble Machine Learning Model. In recent years, there has been significant interest in renewable energy predictions using machine learning and deep learning-based models. However, it is still an area of research where there is a lot of work that needs to be done.

Objectives

The primary objectives of this study are as follows:

• Improve wind energy prediction accuracy using a Stacking Ensemble Machine Learning Model.
• Explore the benefits of ensemble learning in capturing diverse patterns and enhancing overall prediction performance.
• Compare the results of the proposed model with existing methods to evaluate its effectiveness.
• Compare the Stacking Model with other Ensemble Techniques.
• Use SHAP Analysis to determine the importance of each feature in predicting the output.

Base Learners

In this study, we utilize the following base learners to enhance wind energy predictions:

Random Forest Regressor

Random Forest (RF) regressor is an extensively tried ensemble learning model. It is a part of the bagging family of algorithms. The central idea of this algorithm is the random selection of data samples and node characteristic parameters, which is why it is called random forest. RF carries powerful generalization ability and has quick model training.

Gradient Boosted Decision Tree Regressor

Gradient Boosted Decision Tree (GBDT) regressor is a machine learning algorithm that belongs to the boosting family. It works by adding models and the remainders created by continuous reduction training procedures to train the model. The algorithm uses the inverse gradient of the loss function as the residual assumptions. One of the main advantages of GBDT is its ability to provide precise predictions for various types of features.

CatBoost Regressor (CBR)

CatBoost Regressor (CBR) is one of the gradient boosting techniques that can perform both tasks, classification and regression. In this technique, the base weak learner is a decision tree, and gradient boosting is used to fit a sequence of such trees. The training dataset and the gradients for choosing the best tree structure are selected randomly, which prevents overfitting and increases the robustness.

Multilayer Perceptrons (MLP)

Multilayer Perceptrons (MLP) have been used to solve non-linear regression problems for a long time. They are computationally efficient and have the capability to provide predictions with high accuracy. However, they can sometimes be too powerful, resulting in overfitting and smaller mean square error.

These base learners will be combined in a stacking ensemble model to leverage their individual strengths and improve the accuracy of wind energy predictions.

Evaluation Metrics

The evaluation metrics used for this research are:

• Mean Absolute Error (MAE): This is the average of the absolute differences between the predicted and actual values. It measures the average magnitude of the errors in the predicted values. The lower the MAE, the better the model.

• Root Mean Squared Error (RMSE): It is a metric used to evaluate the accuracy of a predictive model. It is calculated as the square root of the Mean Squared Error (MSE). RMSE measures the average magnitude of the errors between the predicted values and the actual values. A lower RMSE indicates that the model has lower error and is therefore a better predictor.

• R-squared (R2): This is the proportion of the variance in the dependent variable that is explained by the independent variables in the model. R-squared values range from 0 to 1, with higher values indicating better model performance.

SHAP Analysis

Understanding the inner workings of complex machine learning models is crucial for building trust and gaining insights into their decision-making processes. SHAP analysis helps explain the predictions of black-box models by assigning feature importance scores to each input variable. By quantifying the impact of features on model predictions, SHAP values provide interpretability and enable the identification of key factors driving specific predictions.

Conclusion

• The study proposes a stacking ensemble interpretable machine learning model for improving wind energy prediction.
• The model uses RF, CatBoost, GB, MLP as first-layer base learners and RidgeR as the second-layer meta-learner.
• The stacking model outperforms mainstream machine learning models and other ensemble techniques, with RMSE = 0.306, MAE = 0.228, and R(square) = 0.970.
• SHAP analysis shows that WS10M_MIN and WS10M_MAX are the most significant variables in the output of the stacked model.
• The model has a range of percentage error in December 2022 month between -21.75019 to 11.70050.
• The study demonstrates the potential of stacking ensemble machine learning models in advancing the field of renewable energy prediction.