Related papers: A Dynamical Systems Approach to Energy Disaggregat…
The issue of estimating the detailed appliance level load consumption has received considerable attention. This paper first presents a Labelled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED), which can be used for research…
Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine…
In order to increase the electric energy efficiency of production machines, it is necessary to determine the energy demand of the constituent electric loads. Therefore, a new measurement system based on nonintrusive load monitoring is…
While non-parametric models, such as neural networks, are sufficient in the load forecasting, separate estimates of fixed and shiftable loads are beneficial to a wide range of applications such as distribution system operational planning,…
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and…
One of the most far-reaching use cases of the internet of things is in smart grid and smart home operation. The smart home concept allows residents to control, monitor, and manage their energy consumption with minimum loss and…
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and…
Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of…
Demand response services at the distribution level are emerging as enabling strategies for improving grid reliability in the presence of intermittent renewable generation and grid congestion. For residential loads, space heating and…
Buildings across the world contribute significantly to the overall energy consumption and are thus stakeholders in grid operations. Towards the development of a smart grid, utilities and governments across the world are encouraging smart…
We were interested in solving a power disaggregation problem which comes down to estimating the power consumption of each device given the total power consumption of the whole house. We started by looking at the Factorial Hierarchical…
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on…
In recent years, non-intrusive load monitoring (NILM) technology has attracted much attention in the related research field by virtue of its unique advantage of utilizing single meter data to achieve accurate decomposition of device-level…
In this work, a non intrusive load disaggregation scheme is proposed. By using a kernel based nonlinear regression strategy, the switching dynamic of an electric network, simulated as a set of RLC circuits with chaotic switching, is…
With the growing demand for energy and increased environmental awareness, Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. By analyzing total power load data, NILM infers the energy…
Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning…
Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most…
With rapid advancements in the Internet of Things (IoT) paradigm, electrical devices in the near future is expected to have IoT capabilities. This enables fine-grained tracking of individual energy consumption data of such devices, offering…
We examine 12 studies on the efficacy of disaggregated energy feedback. The average electricity reduction across these studies is 4.5%. However, 4.5% may be a positively-biased estimate of the savings achievable across the entire population…
A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an…