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The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks. By adding advanced data analytics, these systems can better…
Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior…
This article discusses unexpected consequences of idealistic conceptions about the modernization of power grids. We will focus our analysis on demand-response policies based on automatic decisions by the so-called smart home appliances.…
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 (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…
The need for reducing our energy consumption footprint and the increasing number of electric devices in today's homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device…
A significant amount of research has been conducted in order to make home appliances more efficient in terms of energy usage. Various techniques have been designed and implemented in order to control the power demand and supply. This paper…
In smart grid, the demand side management (DSM) techniques need to be designed to process a large number of controllable loads of several types. In this paper, we proposed a framework to study the demand side management in smart grid which…
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity…
Smart Grid, an intelligent connected grid consisting of millions of smart devices, used to collect data from the grid to improve the efficiency of its operation. These smart devices communicating wirelessly are susceptible to attacks and…
In discussions at the 2015 HICSS meeting, it was argued that loads can provide most of the ancillary services required today and in the future. Through load-level and grid-level control design, high-quality ancillary service for the grid is…
Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. In this way, considerable…
This paper presents an intelligent home energy management system integrated with dispatchable loads (e.g., clothes washers and dryers), distributed renewable generators (e.g., roof-top solar panels), and distributed energy storage devices…
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity…
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…
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand…
This paper deals with home automation systems that are essential for safe and independent living of elderly people. These individuals must be able to perform their Activities of Daily Living (ADLs) without help from caretakers. They must be…
In this work, we present a survey of residential load controlling techniques to implement demand side management in future smart grid. Power generation sector facing important challenges both in quality and quantity to meet the increasing…
Smart meters are an enabling technology for many smart grid applications. This paper introduces a design for a low-cost smart meter system as well as the fundamentals of smart metering. The smart meter platform, provided as open hardware,…
We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM), also known as Energy Disaggregation, through Bayesian Optimization. NIALM offers a cost-effective alternative to…