Detecting Energy Theft and Anomalous Power Usage in Smart Meter Data

The success of renewable energy usage is fuelling the power grids most significant transformation seen in decades, from a centrally controlled electricity supply towards an intelligent, decentralized infrastructure. However, as power grid components become more connected, they also become more vulnerable to cyber attacks, fraud, and software failures. Many recent developments focus on cyber-physical security, such as physical tampering detection, as well as traditional information security solutions, such as encryption, which cannot cover the entire challenge of cyber threats, as digital electricity meters can be vulnerable to software flaws and hardware malfunctions. With the digitalization of electricity meters, many previously solved security problems, such as electricity theft, are reintroduced as IT related challenges which require modern detection schemes based on data analysis, machine learning and forecasting. The rapid advancements in statistical methods, akin to machine learning techniques, resulted in a boosted interest towards concepts to model, forecast or extract load information, as provided by a smart meter, and detect tampering early on. Anomaly Detection Systems discovers tampering methods by analysing statistical deviations from a defined normal behaviour and is commonly accepted as an appropriate technique to uncover yet unknown patterns of misuse. This work proposes anomaly detection approaches, using the power measurements, for the early detection of tampered with electricity meters. Algorithms based on time series prediction and probabilistic models with detection rates above 90% were implemented and evaluated using various parameters. The contributions include the assessment of different dimensions of available data, introduction of metrics and aggregation methods to optimize the detection of specific pattern, and examination of sophisticated threads such as mimicking behaviour. The work contributes to the understanding of significant characteristics and normal behaviour of electric load data as well as evidence for tampering and especially energy theft

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Breadth analysis of Online Social Networks

This thesis is mainly motivated by the analysis, understanding, and prediction of human behaviour by means of the study of their digital fingeprints. Unlike a classical PhD thesis, where you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth analysis on the research topic of complex networks, such as those that humans create themselves with their relationships and interactions. These kinds of digital communities where humans interact and create relationships are commonly called Online Social Networks. Then, (i) we have collected their interactions, as text messages they share among each other, in order to analyze the sentiment and topic of such messages. We have basically applied the state-of-the-art techniques for Natural Language Processing, widely developed and tested on English texts, in a collection of Spanish Tweets and we compare the results. Next, (ii) we focused on Topic Detection, creating our own classifier and applying it to the former Tweets dataset. The breakthroughs are two: our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy, outperforming previous results. After that, (iii) we moved to analyze the network structure (or topology) and their data values to detect outliers. We hypothesize that in social networks there is a large mass of users that behaves similarly, while a reduced set of them behave in a different way. However, specially among this last group, we try to separate those with high activity, or low activity, or any other paramater/feature that make them belong to different kind of outliers. We aim to detect influential users in one of these outliers set. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected os of shape, magnitude, amplitude or combination of those. We applied this method to a subset of roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier users. Finally, (iv) we find interesting to address the monitorization of real complex networks. We created a framework to dynamically adapt the temporality of large-scale dynamic networks, reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.PublicadoPrograma de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Rosa María Benito Zafrilla.- Secretario: Ángel Cuevas Rumín.- Vocal: José Ernesto Jiménez Merin