An Integration of Genetic Feature Selector, Histogram-Based Outlier Score, and Deep Learning for Wind Turbine Power Prediction. Issue 4 (21st December 2022)

• Record Type: Journal Article
• Authors: Fahim, Parastou; Vaezi, Nima; Shahraki, Amin; Khoshnevisan, Mohammad
• Is Part Of: Energy sources. Volume 44:Issue 4(2022)
• Page Start: 9342
• Page End: 9365
• Publication Date: 2022-12-21
• Subjects: Wind turbine -- power prediction -- machine learning -- deep learning -- genetic algorithm; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2022.2129876
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: During the last decades, the importance of clean energy resources is being increased. Wind is one of the most significant clean energy resources. Forecasting the output power of wind turbines is important for turbine control to improve power grids' performance and maintenance. In this study, a novel method for predicting the power of wind turbines is proposed based on integrating data preprocessing, re-sampling, feature selection (genetic algorithm), and outlier detection (Histogram-Based Outlier Score) techniques to prepare the data for the deep learning (DL) algorithms. The results show that, after removing features chosen by the genetic algorithm (GA) method, the mean absolute error (MAE) reduced considerably to 333.7. Integrating Histogram-Based Outlier Score (HBOS) with genetic algorithm (GA) significantly decreased the MAE to 488. Comparing the results with benchmark machine learning algorithms, namely Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regression (×GBR), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Recurrent Neural Networks (RNN) models, shows a remarkable improvement in the accuracy of turbine power prediction for about 78.7, 944.9, 104.7, 1456.6, and 17.1 in mean absolute error (MAE), respectively.