References of "Glauner, Patrick 50009304"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailIntroduction to Detection of Non-Technical Losses using Data Analytics
Glauner, Patrick UL; Meira, Jorge Augusto UL; State, Radu UL et al

Scientific Conference (2017, September)

Electricity losses are a frequently appearing problem in power grids. Non-technical losses (NTL) appear during distribution and include, but are not limited to, the following causes: Meter tampering in ... [more ▼]

Electricity losses are a frequently appearing problem in power grids. Non-technical losses (NTL) appear during distribution and include, but are not limited to, the following causes: Meter tampering in order to record lower consumptions, bypassing meters by rigging lines from the power source, arranged false meter readings by bribing meter readers, faulty or broken meters, un-metered supply, technical and human errors in meter readings, data processing and billing. NTLs are also reported to range up to 40% of the total electricity distributed in countries such as Brazil, India, Malaysia or Lebanon. This is an introductory level course to discuss how to predict if a customer causes a NTL. In the last years, employing data analytics methods such as data mining and machine learning have evolved as the primary direction to solve this problem. This course will compare and contrast different approaches reported in the literature. Practical case studies on real data sets will be included. Therefore, attendees will not only understand, but rather experience the challenges of NTL detection and learn how these challenges could be solved in the coming years. [less ▲]

Detailed reference viewed: 28 (3 UL)
Full Text
Peer Reviewed
See detailIs Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Glauner, Patrick UL; Migliosi, Angelo UL; Meira, Jorge Augusto UL et al

in Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017) (2017, September)

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to ... [more ▼]

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection. [less ▲]

Detailed reference viewed: 44 (6 UL)
Full Text
Peer Reviewed
See detailThe Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Glauner, Patrick UL; Meira, Jorge Augusto UL; Valtchev, Petko UL et al

in International Journal of Computational Intelligence Systems (2017), 10(1), 760-775

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science ... [more ▼]

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future. [less ▲]

Detailed reference viewed: 132 (7 UL)
Full Text
Peer Reviewed
See detailA Simple and Correct Even-Odd Algorithm for the Point-in-Polygon Problem for Complex Polygons
Galetzka, Michael; Glauner, Patrick UL

in Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), Volume 1: GRAPP (2017)

Determining if a point is in a polygon or not is used by a lot of applications in computer graphics, computer games and geoinformatics. Implementing this check is error-prone since there are many special ... [more ▼]

Determining if a point is in a polygon or not is used by a lot of applications in computer graphics, computer games and geoinformatics. Implementing this check is error-prone since there are many special cases to be considered. This holds true in particular for complex polygons whose edges intersect each other creating holes. In this paper we present a simple even-odd algorithm to solve this problem for complex polygons in linear time and prove its correctness for all possible points and polygons. We furthermore provide examples and implementation notes for this algorithm. [less ▲]

Detailed reference viewed: 81 (11 UL)
Full Text
Peer Reviewed
See detailDistilling Provider-Independent Data for General Detection of Non-Technical Losses
Meira, Jorge Augusto UL; Glauner, Patrick UL; State, Radu UL et al

in Power and Energy Conference, Illinois 23-24 February 2017 (2017)

Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in ... [more ▼]

Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses. [less ▲]

Detailed reference viewed: 86 (20 UL)
Full Text
Peer Reviewed
See detailThe Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Glauner, Patrick UL; Du, Manxing UL; Paraschiv, Victor et al

in Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) (2017)

Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we ... [more ▼]

Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research. [less ▲]

Detailed reference viewed: 79 (13 UL)
Full Text
Peer Reviewed
See detailIdentifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Glauner, Patrick UL; Dahringer, Niklas; Puhachov, Oleksandr et al

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging ... [more ▼]

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software. [less ▲]

Detailed reference viewed: 36 (8 UL)
Full Text
Peer Reviewed
See detailDeep Learning on Big Data Sets in the Cloud with Apache Spark and Google TensorFlow
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2016, December 09)

Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine ... [more ▼]

Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to selflearn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging data analytics problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning in a cloud using a distributed environment for Big Data analytics. This combines Apache Spark and TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications. Practical demonstrations will include character recognition and time series forecasting in Big Data sets. Attendees will be provided with code snippets that they can easily amend in order to analyze their own data. A related, but shorter tutorial focusing on Deep Learning on a single computer was given at the Data Science Luxembourg Meetup in April 2016. It was attended by 70 people making it the most attended event of this Meetup series in Luxembourg ever since its beginning. [less ▲]

Detailed reference viewed: 235 (4 UL)
Full Text
Peer Reviewed
See detailLoad Forecasting with Artificial Intelligence on Big Data
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2016, October 09)

In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers ... [more ▼]

In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging signal processing problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning for load forecasting with TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications. The advantage of Deep Learning is that the results can easily be applied to other problems, such as detection of nontechnical losses. Attendees will be provided with code snippets that they can easily amend in order to perform analyses on their own time series. [less ▲]

Detailed reference viewed: 122 (4 UL)
Full Text
See detailDeep Learning Concepts from Theory to Practice
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2016, January 19)

Detailed reference viewed: 101 (8 UL)
Full Text
Peer Reviewed
See detailDetecting Electricity Theft
Glauner, Patrick UL

Poster (2016)

Detailed reference viewed: 65 (11 UL)
Full Text
Peer Reviewed
See detailNeighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Glauner, Patrick UL; Meira, Jorge Augusto UL; Dolberg, Lautaro et al

in Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016) (2016)

Electricity theft occurs around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, electricity theft belongs to non ... [more ▼]

Electricity theft occurs around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, electricity theft belongs to non-technical losses (NTL), which occur during the distribution of electricity in power grids. In this paper, we build features from the neighborhood of customers. We first split the area in which the customers are located into grids of different sizes. For each grid cell we then compute the proportion of inspected customers and the proportion of NTL found among the inspected customers. We then analyze the distributions of features generated and show why they are useful to predict NTL. In addition, we compute features from the consumption time series of customers. We also use master data features of customers, such as their customer class and voltage of their connection. We compute these features for a Big Data base of 31M meter readings, 700K customers and 400K inspection results. We then use these features to train four machine learning algorithms that are particularly suitable for Big Data sets because of their parallelizable structure: logistic regression, k-nearest neighbors, linear support vector machine and random forest. Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%. This work can therefore be deployed to a wide range of different regions. [less ▲]

Detailed reference viewed: 68 (7 UL)
Full Text
Peer Reviewed
See detailLarge-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Glauner, Patrick UL; Boechat, Andre; Dolberg, Lautaro et al

in Proceedings of the Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016) (2016)

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires ... [more ▼]

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets. [less ▲]

Detailed reference viewed: 62 (6 UL)
Full Text
Peer Reviewed
See detailDeep Learning For Smile Recognition
Glauner, Patrick UL

in Proceedings of the 12th International FLINS Conference (FLINS 2016) (2016)

Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A ... [more ▼]

Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU. [less ▲]

Detailed reference viewed: 66 (25 UL)