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NON-INTRUSIVE LOAD MONITORING AND DEMAND RESPONSE FOR RESIDENTIAL ENERGY MANAGEMENT
Title
NON-INTRUSIVE LOAD MONITORING AND DEMAND RESPONSE FOR RESIDENTIAL ENERGY MANAGEMENT
Creator
Iwayemi, Abiodun
Date
2016, 2016-05
Description
Compared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque....
Show moreCompared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque. Residential electricity customers receive a monthly bill detailing their aggregate energy usage, without any insight into which appliances are responsible for what proportions of their bill. We therefore created a Non-intrusive load monitoring framework that uses only data available from smart meters and the price signals from the Electric utility, and combine it with Optimal Stopping Rule-based schedulers to create a framework to equip residents with the information they need to be more energy efficient while balancing their costs and comfort. Non-intrusive load monitoring provides homeowners with detailed feedback on their electricity usage, but an open area is automated appliance labeling and the creation of generalizable appliance models that can be trained in one home, and deployed in another. Manually labeling such events to use them for disaggregating residential appliances is a costly and tedious task, and we developed two approaches for semisupervised learning of appliance signatures. The first approach uses 1-Nearest neighbor semi-supervised learning, and we developed a stopping criterion which reduces the likelihood of mislabeling appliance instances. This approach was extended to a cluster-then-label semi-supervised learning approach which can use only 3 labeled samples of each appliance to label and classify similar appliances within the home. Our approach enables the comparison of unequal length time series, and incorporates additional features extracted from the appliance time series. Finally, we develop a hybrid framework that combines detailed appliance models learned via Non-intrusive load monitoring with optimal stopping rule schedulers. We evaluated the performance of these models in terms of cost and delay, and explored the effect that errors in the real-time price and appliance models have on appliance running costs to demonstrate how our approach outperforms scheduling using only day head prices.
Ph.D. in Electrical Engineering, May 2016
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APPLICATION OF MACHINE LEARNING TO ELECTRICAL DATA ANALYSIS
Title
APPLICATION OF MACHINE LEARNING TO ELECTRICAL DATA ANALYSIS
Creator
Bao, Zhen
Date
2017, 2017-05
Description
The dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs,...
Show moreThe dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs, cloud storage based power consumption management in internet data center, identifying hot socket problem in smart meters, and online event detection for non-intrusive load monitoring without knowing label. Mathematical models are constructed to fulfill the research of the four targets, and numerical examples are used to test the effectiveness of the models. The first two parts optimize jobs in Data Center in order to find the best way of utilizing the existing computing resources and storage. Mixed-integer programming (MIP) is used in the formulation. The purpose of the third part is to identify the hot socket problem in smart meter. Machine learning method has been used to locate the bad installation of smart meters by analyzing historical data from smart meters. The fourth part is non-intrusive load monitoring for residential load in houses. Signal processing and deep learning methods are used to identify the specific loads from high frequency signals.
Ph.D. in Electrical Engineering, May 2017
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