The Experts below are selected from a list of 2409 Experts worldwide ranked by ideXlab platform
William J Knottenbelt - One of the best experts on this subject based on the ideXlab platform.
-
the uk dale dataset Domestic Appliance level electricity demand and whole house demand from five uk homes
Scientific Data, 2015Co-Authors: Jack Kelly, William J KnottenbeltAbstract:Many countries are rolling out smart electricity meters. These measure a home’s total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, Appliance-by-Appliance consumption information. Energy disaggregation is a computational technique for estimating Appliance-by-Appliance energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the ‘ground truth’ demand of individual Appliances. In this context, we present UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual Appliances. This is the first open access UK dataset at this temporal resolution. We recorded from five houses, one of which was recorded for 655 days, the longest duration we are aware of for any energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset. Machine-accessible metadata file describing the reported data (ISA-Tab format)
Squartini Stefano - One of the best experts on this subject based on the ideXlab platform.
-
Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring
IEEE, 2018Co-Authors: Valenti Michele, Bonfigli Roberto, Principi Emanuele, Squartini StefanoAbstract:Non-intrusive load monitoring (NILM) is defined as the task of retrieving the active power consumption of two or more Appliances from information gathered at a single metering point. In this work, the use of the reactive aggregate power as an additional feature to the commonly used active power for deep neural models is proposed. The NILM problem is formulated as a denoising problem, and denoising autoencoder (dAE) neural architectures are used to estimate the Appliances individual active power consumption. The proposed approach is evaluated on two public datasets: the Almanac of Minutely Power dataset (AMPds) and the UK Domestic Appliance-Level Electricity (UK-DALE) dataset. In order to better evaluate the generalization capabilities of the algorithm, different testing conditions are considered for the UK-DALE dataset, namely a seen and an unseen scenario. The results show that introducing the reactive power can indeed bring and overall performance increase in all scenarios, ranging from +4.9% to +8.4% of the energy-based F1 score
Alan F. Blackwell - One of the best experts on this subject based on the ideXlab platform.
-
the fuzzy felt ethnography understanding the programming patterns of Domestic Appliances
Ubiquitous Computing, 2004Co-Authors: Jennifer A. Rode, Eleanor F. Toye, Alan F. BlackwellAbstract:In this paper, we discuss Domestic Appliance use based on an ethnographic study of nine households. Specifically, we look at which Domestic Appliances users choose to “program”, and break them into two categories for analysis; those that allow users to program actions for the future and those that allow for macro creation to make repeated tasks easier. We also look at Domestic programming habits based on gender.
Ming Jiang - One of the best experts on this subject based on the ideXlab platform.
-
risk driven smart home resource management using cloud services
Future Generation Computer Systems, 2014Co-Authors: Tom Kirkham, Django Armstrong, Karim Djemame, Ming JiangAbstract:In order to fully exploit the concept of Smart Home, challenges associated with multiple device management in consumer facing applications have to be addressed. Specific to this is the management of resource usage in the home via the improved utilization of devices, this is achieved by integration with the wider environment they operate in. The traditional model of the isolated device no longer applies, the future home will be connected with services provided by third parties ranging from supermarkets to Domestic Appliance manufacturers. In order to achieve this risk based integrated device management and contextualization is explored in this paper based on the cloud computing model. We produce an architecture and evaluate risk models to assist in this management of devices from a security, privacy and resource management perspective. We later propose an expansion on the risk based approach to wider data sharing between the home and external services using the key indicators of TREC (Trust, Risk, Eco-efficiency and Cost). The paper contributes to Smart Home research by defining how Cloud service management principles of risk and contextualization for virtual machines can produce solutions to emerging challenges facing a new generation of Smart Home devices.
Atif Alamri - One of the best experts on this subject based on the ideXlab platform.
-
Mining Human Activity Patterns from Smart Home Big Data for Health Care Applications
IEEE Access, 2017Co-Authors: Abdulsalam Yassine, Shailendra Singh, Atif AlamriAbstract:Nowadays, there is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Conse- quently, cities around theworld are investing heavily in digital transformation in an effort to provide healthier ecosystems for people. In such a transformation, millions of homes are being equipped with smart devices (e.g., smart meters, sensors, and so on), which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using a shower/bath. This paper addresses the need to analyze temporal energy consumption patterns at the Appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this paper uses the U.K. Domestic Appliance Level Electricity data set—time series data of power consumption collected from 2012 to 2015 with the time resolution of 6 s for five houses with 109 Appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 h, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from Appliance usage are presented in detail in this paper along with the accuracy of short- and long-term predictions.