Metadata for Energy Disaggregation

Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand. Energy disaggregation researchers require datasets of the power demand from individual appliances and the whole-home power demand. Multiple such datasets have been released over the last few years but provide metadata in a disparate array of formats including CSV files and plain-text README files. At best, the lack of a standard metadata schema makes it unnecessarily time-consuming to write software to process multiple datasets and, at worse, the lack of a standard means that crucial information is simply absent from some datasets. We propose a metadata schema for representing appliances, meters, buildings, datasets, prior knowledge about appliances and appliance models. The schema is relational and provides a simple but powerful inheritance mechanism.Comment: To appear in The 2nd IEEE International Workshop on Consumer Devices and Systems (CDS 2014) in V\"aster{\aa}s, Swede

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy

Evaluation of the management of Hr-HPV+/PapTest- women. Results at 1-year recall

With cervical cancer screening the choice of 1-year as a period of follow-up in positive high-risk HPV women without cytological lesions is still under discussion. We evaluated the management of these women and the role of HPV genotyping test. We did a cervical cancer screening study of women aged 35-64 with primary high-risk HPV test. Women positive for high-risk HPV with negative cytology were followed-up after 1 year. In this study we selected women with high-risk HPV+/PapTest- resulted high-risk HPV+ at recall and performed the PapTest and HPV genotyping test. The detection rate of squamous high grade (CIN2+) relative to the total screened cohort was 2.1‰, and it was 0.2‰ at the 1-year recall. The colposcopy performed in women referred at the 1-year recall accounted for 48.8% of the total (baseline + 1-year recall), and 84.3% of these women had no cytological lesions. The most frequent hr-HPV genotype detected was HPV16 and 66.7% of co-infections were due to HPV16 and HPV18. 54.5% of women presented a persistent infection at 1-year recall with the same HPV subtype, 50% of persistent infections was due to HPV16 and 16.7% of these were determined to be CIN2+ histological lesions. Our data show that it may be useful to extend the period of follow-up for women hr-HPV+/PapTest- so as to reduce the number of unnecessary colposcopies due to the transitory infections and that the genotyping test could help to identify the persistent infections in which HPV16 is involved

Human papillomavirus genotyping among the women with cervical abnormalities in Ulaanbaatar, Mongolia

学位記番号：保博甲12