Related papers: A Comprehensive Review on the NILM Algorithms for …
Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A non-invasive measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a…
Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recently several studies have proposed a new approach based on…
This article introduces a modified simulated annealing optimization approach for automatically determining optimal energy management strategies in grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings and…
In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed…
Performing analytic of household load curves (LCs) has significant value in predicting individual electricity consumption patterns, and hence facilitate developing demand-response strategy, and finally achieve energy efficiency improvement…
Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent…
The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of…
In this paper we present a systematic review of various home energy management (HEM) schemes. Employment of home energy management programs will make the electricity consumption smarter and more efficient. Advantages of HEM include,…
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life…
In smart electricity grids, high time granularity (HTG) power consumption data can be decomposed into individual appliance load signatures via Nonintrusive Appliance Load Monitoring techniques to expose appliance usage profiles. Various…
As buildings become increasingly connected and sensor-rich, intelligent remote heating control is rapidly superseding conventional local heating control. Such control algorithms often aim at reducing energy consumption by minimizing…
The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe…
While the large energy consumption of Large Language Models (LLMs) is recognized by the community, system operators lack guidance for energy-efficient LLM inference deployments that leverage energy trade-offs of heterogeneous hardware due…
Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case…
Electrical load classification is generally divided into intrusive and non-intrusive approaches, both having their limitations and advantages. With the non-intrusive approach, controlling appliances is not possible, but the installation…
Load disaggregation techniques infer the operation of different power consuming devices from a single measurement point that records the total power draw over time. Thus, a device consuming power at the moment can be understood as…
Smart buildings are the need of the day with increasing demand-supply ratios and deficiency to generate considerably. In any modern non-industrial infrastructure, these demands mainly comprise of thermostatically controlled loads (TCLs),…
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for…
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…
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…