Machine learning ensembles for wind power prediction

Heinermann, Justin; Krämer, Oliver

doi:10.1016/j.renene.2015.11.073

Cited by 206 publications

(85 citation statements)

References 16 publications

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“…Support vector machines have been used as well for fault detection in power grids [33]. Moreover, research projects have investigated ML methods with supervised techniques for wind turbines power generation [34,35].…”

Section: Scalable Big Data Managementmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

107

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European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

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Section: Scalable Big Data Managementmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

107

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“…Javed et al [21] presented an ensemble of summation wavelet extreme learning machine (SW-ELME) models with incremental learning scheme to predict tool wear, and this model can be used for estimating tool life span and giving confidence for decision-making. ese works [16][17][18][19][20][21] demonstrated the high accuracy of ensemble predictions in tool wear prediction. However, the ensemble strategies used in these studies are all based on bagging [22] or AdaBoost [23].…”

Section: Introductionmentioning

confidence: 98%

“…us, the ensemble learning which significantly improves the generalization ability of a single classifier and gives better results than a single classifier [15] can be considered a viable alternative for obtaining the classifier for tool wear state recognition. e idea of ensemble learning method is to build a classifier predictive model by integrating multiple single classifiers [16]. Yu [17] proposed a discrete particle swarm optimization algorithmbased selective ANN ensemble (DPSOEN) approach to predict tool wear, and this method had better generalization performance than single ANN.…”

Section: Introductionmentioning

confidence: 99%

Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Yang

Mei

Jiang

et al. 2019

Shock and Vibration

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Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

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“…2 General process of applying supervised machine learning to a problem (After Kotsiantis, 2007). (Figueiredo et al, 2011;Ni et al, 2005;Nick et al, 2009;Santos et al, 2016;Worden and Manson, 2007), construction materials (Cheng et al, 2012;Chou et al, 2014;Sonebi et al, 2016), wind energy (Becker and Thrä n, 2017;Clifton et al, 2013;Heinermann and Kramer, 2016), and transportation engineering (Liu et al, 2018a(Liu et al, , 2018b. However, the application of ML techniques in wind engineering is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

Predicting wind pressures around circular cylinders using machine learning techniques

Kwok

2020

Journal of Wind Engineering and Industrial Aerodynamics

104

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Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20 th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 10 4 to 10 6 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide very efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around smooth circular cylinders within the studied Re and Ti range.

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Machine learning ensembles for wind power prediction

Cited by 206 publications

References 16 publications

Big Data for Energy Management and Energy-Efficient Buildings

Big Data for Energy Management and Energy-Efficient Buildings

Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Predicting wind pressures around circular cylinders using machine learning techniques

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