A supervised ensemble learning method for fault diagnosis in photovoltaic strings

Kapucu, Ceyhun; Çubukçu, Mete

doi:10.1016/j.energy.2021.120463

Cited by 60 publications

(32 citation statements)

References 26 publications

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“…They give the averaged prediction of these combined trees. In contrast to the RF, ET model uses a complete training dataset rather than re-sampled replicas to grow the trees and split the tree nodes randomly (Kapucu & Cubukcu, 2021 ). LR is a statistical model that employs a logistic function to model a binary dependent variable.…”

Section: Methodsmentioning

confidence: 99%

“…The use of ensemble learning is recommended to increase the stability of a single learning algorithm and its prediction accuracy (Dietterich, 2000 ). The ensemble learning model is based on a meta-algorithm that combines the same or different types of individually trained models to generates a final prediction (Kapucu & Cubukcu, 2021 ). Ensemble learning can be classified according to the variety of basic learning algorithms included in the created model and the way the model is created.…”

Section: Introductionmentioning

confidence: 99%

“…Second, ensemble learning can be divided to averaging methods and boosting methods. In averaging methods, learning models are created independently (Kapucu & Cubukcu, 2021 ). The predictions produced by these basic models are averaged to reduce the variance.…”

Section: Introductionmentioning

confidence: 99%

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Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Karalar

Kapucu

Gürüler

2021

Int J Educ Technol High Educ

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Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make instructional interventions to avoid leaving them behind. This goal can be achieved by new data mining techniques and machine learning methods. This study took both the synchronous and asynchronous activity characteristics of students into account to identify students at risk of academic failure during the pandemic. Additionally, this study proposes an optimal ensemble model predicting students at risk using a combination of relevant machine learning algorithms. Performances of over two thousand university students were predicted with an ensemble model in terms of gender, degree, number of downloaded lecture notes and course materials, total time spent in online sessions, number of attendances, and quiz score. Asynchronous learning activities were found more determinant than synchronous ones. The proposed ensemble model made a good prediction with a specificity of 90.34%. Thus, practitioners are suggested to monitor and organize training activities accordingly.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Karalar

Kapucu

Gürüler

2021

Int J Educ Technol High Educ

Self Cite

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“…For the training of each model, the k cross-validation with k = 10 is used to prevent overfitting, while the Grid Search (GS) [49,50] is used for hyperparameters optimization. Table 2 shows the set of hyperparameters used for each model and the values range.…”

Section: Fault Diagnosismentioning

confidence: 99%

Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery

et al. 2021

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Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time.

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“…Solar PV project costs, which are now lower than marginal fossil-fuel prices on a worldwide scale, are expected to continue to fall in the coming decades [68].…”

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confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

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A supervised ensemble learning method for fault diagnosis in photovoltaic strings

Cited by 60 publications

References 26 publications

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery

An Effective Evaluation on Fault Detection in Solar Panels

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