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Was Bits & Bäume verbindet: Digitalisierung nachhaltig gestalten, ISBN:978-3-96238-149-3

A large fraction of energy consumed in households is due to space heating. Especially during daytime, the heating is often running constantly, controlled only by a thermostat – even if the inhabitants are not present. Taking advantage of the absence of the inhabitants to save heating energy by lowering the temperature thus poses a great opportunity. Since the concrete savings of an occupancy-based heating strategy strongly depend on the individual occupancy pattern, a fast and inexpensive method to quantify these potential savings would be beneficial. In this paper we present such a practical method which builds upon an approach to estimate a household’s occupancy from its historical electricity consumption data, as gathered by smart meters. Based on the derived occupancy data, we automatically calculate the potential savings. Besides occupancy data, the underlying model also takes into account publicly available weather data and relevant building characteristics. Using this approach, households with high potential for energy savings can be quickly identified and their members could be more easily convinced to adopt an occupancy-based heating strategy (either by manually adjusting the thermostat or by investing in automation) since their monetary benefits can be calculated and the risk of misinvestment is thus reduced. To prove the usefulness of our system, we apply it to a large dataset containing relevant building and household data such as the size and age of several thousand households and show that, on average, a household can save over 9% heating energy when following an occupancy-based heating regime, while certain groups, such as single-person households, can even save 14% on average.

This report presents a simulation framework based on the ISO 13790 5R1C lumped capacitance building model for the evaluation of the energy savings potential of occupancy prediction algorithms. We show the derivation of the model parameters and introduce a new methodology to prepare weather data for simulating the energy consumption of a heating system when predictively controlling the thermostat.

Increasing number of appliances drives residential energy consumption

When it comes to conserving electricity, it is crucial for users to know how much energy is consumed by individual appliances. However, the technical feedback provided by existing energy consumption feedback systems in the form of dry numbers and intangible units is not appropriate for most users. To address this shortcoming we developed PowerPedia, a system that provides behavior-influencing feedback over and above pure consumption values. By integrating a community platform - a Wikipedia for electrical appliances - PowerPedia enables users to identify and compare the consumption of their domestic appliances with that of others. It thus helps users to better understand their electricity consumption and take effective action to save electricity

Technischer Bericht / Departement Informatik, ETH Zürich, 507

In this paper we introduce a new distributed algorithm for location discovery. It can be used in wireless ad-hoc sensor networks that are equipped with means of measuring the distances between the nodes (like the intensity of the received signal strength). The algorithm takes the reliability of measurements into account during calculation of the nodes positions. Simulation results are presented, showing the algorithms performance in relation to its accuracy, communication and calculation costs. The simulation results of our approach yield 2 to 4 times better results in position accuracy than other systems described previously. This level of performance can be reached using only few broadcast messages with small and constant size, for each node in the network.

Arbeitsbericht

The paper shows that characterizing the causal relationship between significant events is an important but non-trivial aspect for understanding the behavior of distributed programs. An introduction to the notion of causality and its relation to logical time is given; some fundamental results concerning the characterization of causality are pre- sented. Recent work on the detection of causal relationships in distributed computations is surveyed. The relative merits and limitations of the different approaches are discussed, and their general feasibility is analyzed.