A Comparative Analysis of XGBoost and Temporal Convolutional Network Models for Wind Power Forecasting

Phan, Quoc-Thang; Wu, Yuan‐Kang; Dzung, Phan Quoc

doi:10.1109/is3c50286.2020.00113

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…Based on tree-boosting machine learning algorithms, XGBoost ensures a more harmonious balance between bias and variance, resulting in a more optimal “bias-variance” trade-off. In addition, XGBoost shows excellent performance, especially on large datasets, and manages to be fast in execution, making it favorable for real-world applications [ 68 ].…”

Section: Anomaly Detection Methods In Sensor Data Environmentsmentioning

confidence: 99%

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Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Bałdyga,

Barański,

Belter

et al. 2024

Sensors

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To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging infrastructure, and adverse environmental conditions on the one hand, and the scale, complexity, and critical safety implications of railway systems on the other. Our study is underpinned by the three objectives. Specifically, we aim to identify time series anomaly detection methods applied to railway sensor device data, recognize the advantages and disadvantages of these methods, and evaluate their effectiveness. To address the research objectives, the first part of the study involved a systematic literature review and a series of controlled experiments. In the case of the former, we adopted well-established guidelines to structure and visualize the review. In the second part, we investigated the effectiveness of selected machine learning methods. To evaluate the predictive performance of each method, a five-fold cross-validation approach was applied to ensure the highest accuracy and generality. Based on the calculated accuracy, the results show that the top three methods are CatBoost (96%), Random Forest (91%), and XGBoost (90%), whereas the lowest accuracy is observed for One-Class Support Vector Machines (48%), Local Outlier Factor (53%), and Isolation Forest (55%). As the industry moves toward a zero-defect paradigm on a global scale, ongoing research efforts are focused on improving existing methods and developing new ones that contribute to the safety and quality of rail transportation. In this sense, there are at least four avenues for future research worth considering: testing richer data sets, hyperparameter optimization, and implementing other methods not included in the current study.

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Section: Anomaly Detection Methods In Sensor Data Environmentsmentioning

confidence: 99%

“…Finally, a significant drawback is the amount of computation required during the tuning phase. As parameter tuning becomes essential to optimize model performance, it can consume over 99.9% of computational resources, underlining its resource-intensive nature [ 68 ].…”

Section: Anomaly Detection Methods In Sensor Data Environmentsmentioning

confidence: 99%

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Bałdyga,

Barański,

Belter

et al. 2024

Sensors

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“…The SVM-based classification algorithm was employed in this work due to its popularity and applications in acoustic scene classification [52]. The rationale for selecting the XGBoost technique was related to its exceptionally good performance, for some applications even outperforming deep learning techniques [53].…”

Section: Discrimination Methods and Their Implementationmentioning

confidence: 99%

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

Zieliński

Antoniuk

Johnson

2022

J AUDIO SPEECH MUSIC PROC.

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One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

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“…where ȳi,t is the prediction of the XGBoost regressor proposed by Chen et al [20], widely used in many applications involving regression tasks, including wind power forecasting as done by Phan et al [21], Cai et al [22], or Phan et al [23]. This evaluation metric expresses the percentage of improvement achieved by the proposed model with respect to a baseline, which in this case is the XGBoost regressor.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks

2023

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Wind energy represents one of the leading renewable energy sectors and is considered instrumental in the ongoing decarbonization process. Accurate forecasts are essential for a reliable large-scale wind power integration, allowing efficient operation and maintenance, planning of unit commitment, and scheduling by system operators. However, due to non-stationarity, randomness, and intermittency, forecasting wind power is challenging. This work investigates a multi-modal approach for wind power forecasting by considering turbine-level time series collected from SCADA systems and high-resolution Numerical Weather Prediction maps. A neural architecture based on stacked Recurrent Neural Networks is proposed to process and combine different data sources containing spatio-temporal patterns. This architecture allows combining the local information from the turbine’s internal operating conditions with future meteorological data from the surrounding area. Specifically, this work focuses on multi-horizon turbine-level hourly forecasts for an entire wind farm with a lead time of 90 h. This work explores the impact of meteorological variables on different spatial scales, from full grids to cardinal point features, on wind power forecasts. Results show that a subset of features associated with all wind directions, even when spatially distant, can produce more accurate forecasts with respect to full grids and reduce computation times. The proposed model outperforms the linear regression baseline and the XGBoost regressor achieving an average skill score of 25%. Finally, the integration of SCADA data in the training process improved the predictions allowing the multi-modal neural network to model not only the meteorological patterns but also the turbine’s internal behavior.

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A Comparative Analysis of XGBoost and Temporal Convolutional Network Models for Wind Power Forecasting

Cited by 17 publications

References 8 publications

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks

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