GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining

Service discovery at home

Service discovery is a fairly new field that kicked off since the advent of ubiquitous computing and has been found essential in the making of intelligent networks by implementing automated discovery and remote control between devices. This paper provides an overview and comparison of several prominent service discovery mechanisms currently available. It also introduces the at home anywhere service discovery protocol (SDP@HA) design which improves on the current state of the art by accommodating resource lean devices, implementing a dynamic leader election for a central cataloguing device and embedding robustness to the service discovery architecture as an important criterion

An In Depth Study into Using EMI Signatures for Appliance Identification

Energy conservation is a key factor towards long term energy sustainability. Real-time end user energy feedback, using disaggregated electric load composition, can play a pivotal role in motivating consumers towards energy conservation. Recent works have explored using high frequency conducted electromagnetic interference (EMI) on power lines as a single point sensing parameter for monitoring common home appliances. However, key questions regarding the reliability and feasibility of using EMI signatures for non-intrusive load monitoring over multiple appliances across different sensing paradigms remain unanswered. This work presents some of the key challenges towards using EMI as a unique and time invariant feature for load disaggregation. In-depth empirical evaluations of a large number of appliances in different sensing configurations are carried out, in both laboratory and real world settings. Insights into the effects of external parameters such as line impedance, background noise and appliance coupling on the EMI behavior of an appliance are realized through simulations and measurements. A generic approach for simulating the EMI behavior of an appliance that can then be used to do a detailed analysis of real world phenomenology is presented. The simulation approach is validated with EMI data from a router. Our EMI dataset - High Frequency EMI Dataset (HFED) is also released

Cooperative tasks between humans and robots in industrial environments

Collaborative tasks between human operators and robotic manipulators can improve the performance and flexibility of industrial environments. Nevertheless, the safety of humans should always be guaranteed and the behaviour of the robots should be modified when a risk of collision may happen. This paper presents the research that the authors have performed in recent years in order to develop a human-robot interaction system which guarantees human safety by precisely tracking the complete body of the human and by activating safety strategies when the distance between them is too small. This paper not only summarizes the techniques which have been implemented in order to develop this system, but it also shows its application in three real human-robot interaction tasks.The research leading to these results has received funding from the European Communityʹs Seventh Framework Programme (FP7/2007‐2013) under Grant Agreement no. 231640 and the project HANDLE. This research has also been supported by the Spanish Ministry of Education and Science through the research project DPI2011‐22766

The Potential for Segmentation of the Retail Market for Electricity in Ireland. ESRI WP433. April 2012

We estimate the gross margin that is earned from the supply of electricity to households in Ireland. Using half hourly electricity demand data, the system marginal price (also called the wholesale price) and the retail price of electricity, we analyse how the gross margin varies across customers with different characteristics. The wholesale price varies throughout the day, thus, the time at which electricity is used affects the gross margin. The main factor in determining gross margin, however, is demand. The highest gross margins are earned from supplying customers that have the following characteristics: being aged between 46 and 55, having a household income of at least €75,000 per annum, being self–employed, having a third level education, having a professional or managerial occupation, living in a household with 7 or more people, living in a detached house, having at least 5 bedrooms or being a mortgage holder. An OLS regression shows that gross margin is partly explained by the energy conservation measures which are present in a household, the number of household members, the number of bedrooms, income, age, occupation and accommodation type

Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively