Multivariate statistical modelling and monitoring of smart buildings
In order to reduce mismatches between real and expected consumption, this thesis explores the use of PCA (Principal Component Analysis) as a modelling tool for buildings. PCA is a statistical technique that allows complex systems to be modelled, and, subsequently, to monitor them to detect abnormal...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/669279 |
| Acceso en línea: | http://hdl.handle.net/10803/669279 |
| Access Level: | acceso abierto |
| Palabra clave: | Smart buildings Edificis intel·ligents Edificios inteligentes Monitoring Monitoratge Monitorización Multivariate statistics Estadística multivariant Estadística multivariante Modelling Modelatge Modelado PCA Unfold-PCA Anàlisi de components principals Análisis de componentes principales 004 311 620 |
| Sumario: | In order to reduce mismatches between real and expected consumption, this thesis explores the use of PCA (Principal Component Analysis) as a modelling tool for buildings. PCA is a statistical technique that allows complex systems to be modelled, and, subsequently, to monitor them to detect abnormal behaviours with respect to the conditions initially modelled. The work in this thesis includes adapting PCA to take full advantage of its potential in buildings. Such adaption is also verified by applying it to various use cases |
|---|