Open Access

The worldwide transformation of electricity production from fossil and nuclear energy sources to renewable energy sources is accompanied by many challenges. One of those challenges is finding an equilibrium of supply and demand – an important balance for the stability of electric grids. Production and consumption are kept in balance by adapting electricity production to consumption. Most renewable energy sources do not produce energy when demanded, but when natural conditions are suitable. As energy cannot yet be stored efficiently, over-production is as much of a problem as underproduction. Demand Response (DR) is the means for end-users to contribute to the balancing challenge. Providing the end-users with an incentive such as time-based pricing that changes according to the supply will encourage users to contribute to the equilibrium. Users’ contribution usually has implications for their daily habits and can be associated with discomfort, thus it requires a high level of involvement. Lowering the required involvement is therefore seen as an important step toward an acceptance of time-based pricing. Since the 1980s, machine learning has been seen as a solution to lower the barrier for private households to participate. The idea is to provide households with information about their electricity consumption, make recommendations on behavior changes or take automated actions. Such information can be retrieved from monitoring a household’s electricity consumption. This thesis contributes to the process of extracting information and knowledge from monitored electricity consumption in private households using machine learning. An overview of data sources and general approaches is provided. Based on this research, the Machine Learning Demand Response Model (MLDR) is introduced, defining the relation between data, knowledge, and actions. This model enhances the understanding of the individual steps required to transform monitored electricity consumption data into individual recommendations or automated actions. These steps are: data monitoring, appliance identification, appliance usage segmentation, and appliance usage prediction. For each of these steps, this thesis provides an overview of the current research state and introduces new approaches. A new monitoring system for both individual appliances and household mains is introduced. The system was used to collect a scientific dataset called Domestic Energy Demand Dataset of Individual Appliances in Germany (DEDDIAG). It contains measurements of 50 individual appliances located in 15 homes, recorded with a sample rate of 1 Hz over a period of up to 3.5 years. The dataset has been enriched with manual appliance usage annotations as well as demographic data describing the household. The system, as well as the dataset, has been published under an open-source license. Based on this dataset, an appliance category identification algorithm is introduced. The algorithm extracts features using a wavelet transformation and classifies data using the k-Nearest-Neighbor (kNN) classifier. It was evaluated and published as a challenge baseline for DEDDIAG. Next to this approach that relies on low sample rates, a high sample rate algorithm is introduced. The algorithm is based on transforming one voltage-current cycle, known as the voltage-current (V-I) trajectory, into two separate Recurrence Plots (RPs) which are then classified using a Convolutional Neural Network (CNN) in combination with Spacial Pyramid Pooling (SPP). The al-gorithm is evaluated on three different datasets and compared to previously proposed algorithms. Finding the start and stop of an appliance is the basis for deriving usage patterns. This appliance event segmentation has received little attention from other researchers, and the most commonly used algorithm, a lower-bound thresholding approach, has never been evaluated. Using the manual annotations created for DEDDIAG, this approach is evaluated using a newly introduced performance metrics called Jaccard-Time-Span-Event-Score (JTES). Together with this, a new segmentation algorithm using Support Vector Machine (SVM) is presented. Finally, based on the usage events that were determined, a combined statistical model for appliance usage prediction is introduced. It predicts future appliance usage based on the preferred time of day and the elapsed time since it was used last. It is evaluated on the GREEND dataset as well as the DEDDIAG. The thesis concludes with an outlook of potential future work.

Das Buch bietet eine umfassende und praxisorientierte Einführung in die wesentlichen Grundlagen und Konzepte der Informatik. Es umfasst den Stoff, der typischerweise in den ersten Semestern eines Informatikstudiums vermittelt wird, vertieft Zusammenhänge, die darüber hinausgehen und macht sie verständlich. Die Themenauswahl orientiert sich an der langfristigen Relevanz für die praktische Anwendung. Praxisnah und aktuell werden die Inhalte für Studierende der Informatik und verwandter Studiengänge sowie für im Beruf stehende Praktiker vermittelt.

Mobility management is a key feature of mobile edge computing. We present an edge cloud infrastructure testbed to explore various mobility scenarios. The design objection of this testbed has been a flexible open platform based on commodity hardware that can easily be scaled with more edge devices and compute resources to perform various edge cloud experiments. As first experiments on our testbed, we have investigated the feasibility of task migration among edge devices caused by edge device overload and unpredictable user movements. We describe the migration process and present some measurements to demonstrate the feasibility.

Stuttering is a complex speech disorder identified by repetitions, prolongations of sounds, syllables or words and blockswhile speaking. Specific stuttering behaviour differs strongly,thus needing personalized therapy. Therapy sessions requirea high level of concentration by the therapist. We introduce STAN, a system to aid speech therapists in stuttering therapysessions. Such an automated feedback system can lower the cognitive load on the therapist and thereby enable a more consistent therapy as well as allowing analysis of stuttering over the span of multiple therapy sessions.

Mobile-access edge clouds provide distributed com-pute capacities for low-latency applications. 5G technology willpave the way for such mobile deployment scenarios. In thispaper, we propose an edge cloud infrastructure that supportslow-latency video analysis connected with bandwidth reductionfor a moving group of persons. As example, we consider a mobilebody camera scenario that monitors the situation in a certain areaand transmits it to an operations center. Our discussion focuseson three aspects: mobility support, low-latency video processing,and bandwidth reduction. For this, we propose a mobile edgecloud infrastructure with a central cloud. In order to optimizevideo processing we optimize the edge cloud device assignmentof the cameras depending on their movement by reassigning itto another cloud device. This requires live migration of ongoingvideo analysis between edge devices. Finally we discuss the useof a mobile central cloud.