A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Autor:	Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis, Kleanthis Malialis, Demetrios G. Eliades, Theocharis Theocharides
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Household end-use disaggregation Time series classification Multi-label classification Water monitoring Machine Learning (cs.LG)
Popis:	Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on the aggregated information. As a case study, we apply our methodology to the challenging problem of household water end-use dissagregation when using non-intrusive water monitoring. Our methodology does not require a-priori identification of events, and to our knowledge, it is considered for the first time. We conduct an extensive experimental study using a residential water-use simulator, involving different machine learning classifiers, multi-label classification methods, and successfully demonstrate the effectiveness of our methodology. Comment: 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2022
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3c05d3bb2e259f1dd3f23a1c5851473 https://zenodo.org/record/6683646 Zobrazit plný text záznamu