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

Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine

Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model

USEM: A ubiquitous smart energy management system for residential homes

With the ever-increasing worldwide demand for energy, and the limited available energy resources, there is a growing need to reduce our energy consumption whenever possible. Therefore, over the past few decades a range of technologies have been proposed to assist consumers with reducing their energy use. Most of these have focused on decreasing energy consumption in the industry, transport, and services sectors. In more recent years, however, growing attention has been given to energy use in the residential sector, which accounts for nearly 30% of total energy consumption in the developed countries. Here we present one such system, which aims to assist residential users with monitoring their energy usage and provides mechanisms for setting up and controlling their home appliances to conserve energy. We also describe a user study we have conducted to evaluate the effectiveness of this system in supporting its users with a range of tools and visualizations developed for ubiquitous devices such as mobile phones and tablets. The findings of this study have shown the potential benefits of our system, and have identified areas of improvement that need to be addressed in the future

Enabling Machine Understandable Exchange of Energy Consumption Information in Intelligent Domotic Environments

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontolog