Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Energy Disaggregation"”.
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Energy disaggregation techniques for visualization and improvement of energy efficiency in processes and buildings
About 45% of the energy demand in developed countries is consumed in households and in public and commercial services. For this reason, the improvement of electrical energy efficiency in facilities has attracted much attention in recent decades. This recent pursuit of energy efficiency has …
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Temporal Mining Approaches for Smart Buildings Research
… opportunities have opened up to help conserve energy in residential and commercial buildings. Moreover, the rapid urbanization we are witnessing requires optimized energy distribution. This dissertation focuses on two sub-problems in improving energy conservation; energy disaggregation and …
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An Enhanced Signal Processing Toolbox for Electrical Energy Monitoring
… of literature exists for load identification and energy disaggregation under ideal, healthy conditions. However, a significant value proposition of nonintrusive load monitoring comes from fault detection and diagnostics. Early detection of electromechanical faults aids safety, reduces energy …
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Physically Motivated Feature Development for Machine Learning Applications
… knowledge, for several applications: energy disaggregation, brain cancer prognosis, and landmine detection with seismo-acoustic vibrometry (SAVi) sensors. For event-based energy disaggregation, or the automated process of extracting component specific energy data from a building's …
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ElectriSense: Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home
Imagine an energy feedback system that displays not only your total power consumption, but also continuously shows real-time usage while breaking it down categorically by electrical appliances. In addition, such a system provides personalized and cost-effective energy saving recommendations, for …
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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 …
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Resolution Tricks and Disaggregation Tools for Smart Power Metering
… load monitor (NILM) aims to solve the energy disaggregation problem by incorporating power system analysis, signal processing, and machine learning. This thesis addresses two problems present in state-of-the-art nonintrusive load monitoring research. First, the ability of existing …
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Efficient Algorithms for Mining Large Spatio-Temporal Data
Knowledge discovery on spatio-temporal datasets has attracted<br />growing interests. Recent advances on remote sensing technology mean<br />that massive amounts of spatio-temporal data are being collected,<br />and its volume keeps increasing at an ever faster pace. It becomes<br />critical to …
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Estimating residential hot water consumption from smart electricity meter data
… that residential water heating is among the most energy-intensive aspects of the water sector, domestic hot water use is often poorly quantified. However, water-related energy savings in the residential sector are possible from the implementation of energy-efficient water heaters. Estimating hot …
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Distribution Level Building Load Prediction Using Deep Learning
… grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of …
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Unsupervised disaggregation of low frequency power measurements
… the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates …