Proceedings ArticleDOI
Prediction of events in the smart grid: Interruptions in distribution transformers
Joaquim L. Viegas,Susana M. Vieira,Rui Melício,Hugo A. Matos,João M. C. Sousa +4 more
- pp 436-441
TLDR
In this paper, a system for the prediction of events in the smart grid is proposed, which infers a label indicating if an event is going to occur in a future time window, in a specific asset, from data of events generated by grid assets and exogenous variables (e.g. weather data).Abstract:
This paper proposes a system for the prediction of events in the smart grid. The system infers a label indicating if an event is going to occur in a future time window, in a specific asset, from data of events generated by grid assets and exogenous variables (e.g. weather data). The system design presented follows a sliding-window classification approach, bag-of-words event representation and makes use of random forests models. The systems performance is evaluated in an experimental case study, backed by real data, with the aim of predicting future interruptions in distribution transformers. Performance results indicate that the system is able to deal with highly imbalanced data and validate its adequacy in dealing with the approached problem, achieving up to 0.75 area under the receiver operating characteristic curve in testing.read more
Citations
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Journal ArticleDOI
A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations
TL;DR: A framework for a decision-support system (DSS) that operates within the IoT ecosystem that leverages advanced analytics of electric smart meter network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue is presented.
Journal ArticleDOI
Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers
TL;DR: Different machine learning (ML) approaches are compared for the coordination of EVs charging and LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB.
Journal ArticleDOI
A voted based random forests algorithm for smart grid distribution network faults prediction
TL;DR: A modified version of voted random forest algorithm (VRF) is proposed for enhancing the predicting accuracy of the faults in the smart distribution network by changing the decision process by introducing multiple SVM models for voting model training.
Proceedings ArticleDOI
Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods
Kristian Wang Hoiem,Vemund Santi,Bendik Nybakk Torsater,Helge Langseth,Christian Andre Andresen,Gjert H. Rosenlund +5 more
TL;DR: Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602, and results indicated that rapid voltage changes and voltage dips are easiest to predict among the tested power quality events.
Book ChapterDOI
Machine Learning-Based Social Media Text Analysis: Impact of the Rising Fuel Prices on Electric Vehicles
TL;DR: In this article , the authors analyzed public opinions and what they expressed on Twitter about EVs and used three Machine Learning (ML) models: Random Forest (RF), Decision Tree (DT), and Naïve Bais (NB).
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.