Unsupervised recognition and prediction of daily patterns in heating loads in buildings
This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classificati...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/61847 |
| Acceso en línea: | http://hdl.handle.net/10810/61847 |
| Access Level: | acceso abierto |
| Palabra clave: | pattern recognition unsupervised clustering heating loads daily profiles |
| Sumario: | This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately. |
|---|