Related papers: Active Collaborative Sensing for Energy Breakdown
Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work proposes a novel…
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…
Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming up with a suitable…
We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on…
Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing…
In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped…
The main objective of this paper is to reduce the number of sensor nodes by estimating a trade off between data accuracy and energy consumption for selecting nodes in probabilistic approach in distributed networks. Design…
Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset…
Increases in energy prices and the global goal of mitigating CO2 emissions necessitate the development of intelligent Building Management Systems (BMS) that operate on an energy-efficient basis. Data Centers, buildings and/or group of…
The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its…
Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is…
Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem, aims to interpret the mains user electricity consumption into appliance level measurement. This article presents a new…
Energy Efficiency of a wireless sensor network (WSN) relies on its main characteristics, including hop-number, user's location, allocated power, and relay. Identifying nodes, which have more impact on these characteristics, is, however,…
The reliable detection of environmental molecules in the presence of noise is an important cellular function, yet the underlying computational mechanisms are not well understood. We introduce a model of two interacting sensors which allows…
Load points are one of the most vital parts of power systems. Due to the new load forms and programs introduced in the demand side, the load-serving entities (LSEs) no longer deal with lump loads, but rather with more dynamic, rational and…
Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter)…
Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios include but are not limited to: elderly…
Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effective way to obtain…
Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from…
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.…