Disaggregation Of Residential Home Energy Via Non Intrusive Load Monitoring For Energy Savings And Targeted Demand Response
Download Disaggregation Of Residential Home Energy Via Non Intrusive Load Monitoring For Energy Savings And Targeted Demand Response PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Disaggregation Of Residential Home Energy Via Non Intrusive Load Monitoring For Energy Savings And Targeted Demand Response book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Disaggregation of Residential Home Energy Via Non-intrusive Load Monitoring for Energy Savings and Targeted Demand Response
Residential energy disaggregation is a process by which the power usage of a home is broken down into the consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to the wall. Non-Intrusive Load Monitoring (NILM) is one such disaggregation option. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. Although the technology has existed for many years, the adoption rate of NILM, and other devices that disaggregate energy, has been minimal. This thesis provides details on the potential benefits, both for the customer and utility provider, associated with furthering the adoption of NILM devices and obtaining the disaggregated appliance level energy-use. A broad overview of potential benefits is presented; however, the primary goal of this thesis will be to investigate two benefits of NILM in detail: overall household energy reduction and targeted demand response. First, installation of a NILM device can provide electricity customers information that allows them to become more aware of their energy consumption, and thereby, more energy efficient. A study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device in their home. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. This was validated by a number of analysis methods returning similar results. Aligned with this benefit comes a recommendation for an incentive structure that can reduce the price paid by the consumer and develop a higher adoption rate of NILM devices. Second, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. There is a significant opportunity for utilities to engage their customers based on the time of use of detailed appliances. Multiple metrics are presented in this thesis to quantify the deferrable load opportunity of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics.
Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.
Computational Science and Computational Intelligence
The CCIS book constitutes selected papers accepted in the Research Track on Artificial Intelligence of the 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024, which took place in Las Vegas, NV, USA, during December 11–13, 2024. The 27 full papers included in this book were carefully reviewed and selected from a total of 383 submissions. They were organized in topical sections on large language models and methodologies; deep learning and applications; artificial intelligence and prediction methods; and artificial intelligence, machine learning, applications and alrogithms.