Volume 61, Issue 2-3 - Energy Status Data / Guest Editors: Klemens Böhm, Dominik Werle

Transmission grid congestion is one of the consequences of an increasing power generation from intermittent renewable capacities. These are often installed in the periphery and have rare generation peaks. It thus becomes more complicated to ensure a balanced grid operation at all times. It is necessary to develop holistic strategies for the management of congestion that consider short and long operating horizons. This paper introduces several congestion management mechanisms along minimal analytical and numerical models. These solutions are then discussed in regard to the necessary data availability and their contribution to an improved congestion management strategy. The paper therefore contributes to the development of a research agenda at the intercept between economists and computer scientists in the area of energy informatics.

In this paper, we propose a model-based system architecture for an interoperable blockchain-based local energy market for prosumers in a residential microgrid setting. Based on the Smart Grid Architecture Model our analysis deduced 21 organizational, informational, technical and blockchain requirements for a local energy market and its underlying information system. These are evaluated in the Landau Microgrid case study. We derive, that a clear value proposition for the key stakeholders, standardization of data exchange and communication, and a suitable physical implementation are the major challenges.

We conduct a structured literature review on the concept of local energy markets (LEMs). LEMs have gained increasing attention in the last two decades. Yet, a holistic definition and clear demarcation of LEMs is still missing. The review shows current works to shift their focus from conceptual implementation and design to increasingly realistic applications of LEMs. Secure access to (near) real-time smart meter data is a prerequisite for LEMs. Current research gaps, e. g. the inclusion of network constraints, agent-centric LEM designs or a comparison of market mechanisms, are identified.

Energy data often is available at high temporal resolution, which challenges the scalability of data-analysis methods. A common way to cope with this is to aggregate data to, say, 15-minute-interval summaries. But it often is not known how much information is lost with this, i. e., how good analysis results on aggregated data actually are. In this article, we study the effects of aggregating energy data on clustering. We propose an experimental design to compare a wide range of clustering methods found in literature. We then introduce different ways to compare clustering results obtained with different aggregation schemes. Our evaluation shows that aggregation affects the clustering quality significantly. Finally, we propose guidelines to select an aggregation scheme.

The recent development of renewable energy sources (RES) challenges energy systems and opens many new research questions. Energy System Models (ESM) are important tools to study these problems. However, including RES into ESM strongly increases the model complexity, because one needs to model the fluctuant, weather-dependent electricity production from RES with a high level of granularity. This leads to long execution times. To deal with this issue, our objective is to reduce the input time series of ESM without losing their energy-related key characteristics, such as weather-dependent fluctuations in production or peak demands. This task is challenging, because of the variety and high-dimensionality of the data. We describe a carefully engineered data-processing pipeline to reduce energy time series. We use Self-Organizing Maps, a specific kind of neural network, to select “representative days”. We show that our approach outperforms the existing ones with respect to the quality of ESM results, and leads to a significant reduction of ESM execution times.

Causal loops are loops in cause-effect relations, where, say for two events A , B , the event A is a cause of B and, vice versa, B is a cause of A . Such loops are traditionally ruled out due to potential logical problems, e. g. , where an effect suppresses its own cause. Motivated by our current physical theories, we show that not only causal loops exist that are logically consistent, but that these loops are computationally tame and help to further investigate on the theoretical foundations of time travel. Causal loops do not necessarily pose problems from a logics, computer-science, and physics point of view. This opens their potential applicability in various fields from philosophy of language to computer science and physics.

This article summarizes the dissertation “Face2Face: Realtime Facial Reenactment” by Justus Thies (Eurographics Graphics Dissertation Online, 2017). It shows advances in the field of 3D reconstruction of human faces using commodity hardware. Besides the reconstruction of the facial geometry and texture, real-time face tracking is demonstrated. The developed algorithms are based on the principle of analysis-by-synthesis. To apply this principle, a mathematical model that represents a face virtually is defined. Utilizing this model to synthesize facial imagery, the model parameters are adjusted, such that the synthesized image fits the input image as good as possible. Thus, in reverse, this process transfers the input image to a virtual representation of the face. The achieved quality allows many new applications that require a good reconstruction of the face. One of these applications is the so-called “Facial Reenactment”. Our developed methods show that such an application does not need any special hardware. The generated results are nearly photo-realistic videos that show the transfer of the expressions of one person to another person. These techniques can for example be used to bring movie dubbing to a new level. Instead of adapting the audio to the video, which might also include changes of the text, the video can be post-processed to match the mouth movements of the dubber. Since the approaches that are shown in the dissertation run in real-time, one can also think of a live dubber in a video teleconferencing system that simultaneously translates the speech of a person to another language. The published videos of the projects in this dissertation led to a broad discussion in the media. On the one hand this is due to the fact that our methods are designed such that they run in real-time and on the other hand that we reduced the hardware requirements to a minimum while improving the resulting quality. In fact, after some preprocessing, we are able to edit ordinary videos from the Internet in real-time. Amongst others, we impose a different mimic to faces of prominent persons like former presidents of the United States of America. This led inevitably to a discussion about trustworthiness of video material, especially from unknown source. Most people did not expect that such manipulations are possible, neglecting existing methods that are already able to edit videos (e. g. special effects in movie productions). Thus, besides the advances in real-time face tracking, our projects raised the awareness of video manipulation.

Hijacking user clicks and touch gestures has become a common attack vector and offers a stealthy approach at escalating the privileges of a process without raising red flags among users or AV software. Exploits falling into this category are categorized as clickjacking attacks and have gained increased popularity on mobile devices, Android being the recent victim of a series of UI vulnerabilities. Focusing on the Android OS this paper highlights previous and current UI-based attack vectors and finishes with an overview of security mechanisms, covering both system-wide as well as app-level protection measures.