Elaborating demand side management strategies is crucial for integrating electricity from renewable sources into the electrical grid. Though future demand side will largely depend on an automatic control of larger loads, it is also widely agreed upon that consumer behavior will play an important role as well - be it by purchasing respective automation techniques or by shifting the use of appliances to other times of the day. Doing so, it becomes possible to select households that offer sufficient load shifting potential, and to overcome undirected and thus, expensive campaigns. To our knowledge, this perspective is still under-researched, especially when it comes to clustering methods on load consumption data with a focus on peak detection accuracy to provide customer segmentation. Using the data collected in the Irish CER dataset, which contains readings for more than 4000 residential customers over a period of 18 months at 30-minute intervals, we show that the whole clustering of the time series, with a few adaptations on the usage of the K-Means algorithm, provides better clustering results without sacrificing practical feasibility. Characteristic load profiles allow us to segment the customers, address groups of households with similar consumption patterns and determine on the fly the cluster membership of a given load curve. This will support decision making regarding the investments in load shifting campaigns to prevent over or under-dimensioning linked to peak energy demand.

In order to reduce our society's dependence on fossil fuels, the energy sector has started to undergo massive changes. Information and communications technology (ICT) increasingly plays a key role in this transformation, both on the supply and demand side. While 85% of the residential energy in the DACH region is consumed by space and water heating, the vast majority of work in the energy informatics field does not focus on these two critical end uses. And yet, the discipline could contribute greatly to reducing consumption and emissions in this area - often with a close nexus to electricity. Besides, direct, real-time feedback on hot water consumption has a particularly high savings potential and can further broaden the sphere of impact of the energy informatics discipline. This paper describes a self-powered energy and water meter that provides users with such feedback in the shower. After an earlier proof of concept study with 60 households together with the Swiss Federal Office of Energy that yielded average energy and water savings of 22%, the smart shower meter has been developed into a mass-market compatible application that has been installed in 8,000 households. This was accompanied by a study with 700 households to verify its practical viability, consumer engagement, and effectiveness in the field. The device showcases a practical example of how ICT applications can be successfully implemented at scale to transform consumption patterns in emission-intense domains, also beyond electricity.

The personal transport sector constitutes an important target of energy conservation and emission reduction programs. In this context, eco-feedback technologies that provide information on the driving behavior have shown to be an effective means to stimulate changes in driving in favor of both, reduced costs and environmental impact. This study extends the literature on eco-feedback technologies as it demonstrates that a smartphone application can improve fuel efficiency even under conditions where monetary incentives are not given, i.e. where the drivers do not pay for fuel. The field test, which took place with 50 corporate car drivers, demonstrates an improvement in the overall fuel efficiency by 3.23%. The theoretical contribution underlines the assumption that context-related feedback can favorably influence behavior even without direct financial benefits for the agent. Given the large share of corporate cars, findings are also of high practical importance and motivate future research on eco-driving feedback technologies.

The worldwide adoption of smart meters that measure and communicate residential electricity consumption gives rise to the development of new energy efficiency services. Several particularly promising applications involve the disaggregation of individual appliances within a particular household in terms of their energy demand. In this paper we present an infrastructure and a set of algorithms that make use of smart meters together with smartphones to realize new energy efficiency services (such as itemized electricity bills or targeted energy saving advice). The smartphones, together with a novel filtering approach, much simplify the training process for appliances signature recognition. We also report on the performance of our system that was tested with 8 simultaneous devices, achieving recognition rates of 87%.

Greenhouse gas (GHG) emissions' accumulation in the atmosphere is constantly increasing; since transport sector contributes significantly in rising emissions, substantial action has to be taken, with the support of ICT. Eco-efficient feedback devices, based on smart phone technologies, are becoming more prominent to support and influence drivers to understand their own CO2footprint. This paper seeks to evaluate how eco-efficient feedback technologies and which feedback types can contribute to environmental sustainability. In particular, how these technologies can support drivers to achieve more sustainable driving behavior. Feedback types, which influence drivers, are most notably financial savings shown over a longer period and peer reviews.

Reducing their energy consumption has become an important objective for many people. Consumption transparency and timely feedback are essential to support those who want to adjust their behavior in order to conserve energy. In this work, we propose an interactive system that provides instantaneous feedback concerning the energy usage on household and device level. For that, we used and extended the capabilities of a smart electricity meter, built a web-based API to enable interoperability with other applications, and developed a mobile phone interface that allows users to monitor, control, and measure the consumption of single appliances. Our system illustrates a way how usage barriers can be lowered and how high user involvement can be created. By providing users the electricity feedback needed -- in real-time and on device level -- the system allows for identifying the biggest energy guzzlers and helps users decrease their energy consumption.

This work presents the hardware design of a dynamically reconfigurable function unit (RFU) to accelerate computation-intensive tasks in Medium Access Control (MAC) layers of WLANs. The function unit is integrated in a pipelined 32 bit RISC processor and provides full hardware support for the Advanced Encryption Standard (AES) as specified in upcoming WLAN standards such as IEEE 802.11i. Dynamic reconfiguration allows the processor to use arithmetic components and memory elements of the RFU not only for AES, but also for additional tasks common in the MAC-layer. With our approach it is possible to accelerate Reed-Solomon-Code generation, Cyclic Redundancy Checks as well as other encryption standards like SQUARE, Magenta and Twofish by supporting Galois Field multiplication and table look-ups. The integration of the reconfigurable unit in the processor core results in an architecture that can simultaneously support control-flow and data-flow oriented tasks. This architecture was prototyped onto a Virtex2 FPGA.