1. Unsupervised Clustering for Pattern Recognition of Heating Energy Demand in Buildings Connected to District-Heating Network
- Author
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Mikel Lumbreras, Koldobika Martin-Escudero, Gonzalo Diarce, Roberto Garay-Martinez, and Ruben Mulero
- Subjects
Computer science ,business.industry ,Process (computing) ,Pattern recognition ,Unsupervised Clustering ,Pattern Recognition ,Heating Energy Demand ,Data modeling ,Identification (information) ,ComputingMethodologies_PATTERNRECOGNITION ,Data-Driven Model ,Pattern recognition (psychology) ,Cluster (physics) ,Anomaly detection ,Artificial intelligence ,District-Heating Networks ,Unsupervised clustering ,Cluster analysis ,business - Abstract
This paper presents a novel framework for the identification of different consumption patterns of heating loads of buildings. The approach to analyzing the consumption data is carried out by a combination of unsupervised clustering models. Density based clustering is used for outlier detection in the original dataset and K-means for pattern recognition. The proposed framework is then applied to a real building connected to the district heating in Tartu (Estonia). Three main day-types are identified for the building as an outcome of the clustering process, with different patterns throughout these days. More than 60% of the analyzed Cluster Validation Indexes studied in this paper show that classifying the daily demand profiles in three clusters is the optimal classification.
- Published
- 2021
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