TY - GEN
T1 - Unsupervised clustering for pattern recognition of heating energy demand in buildings connected to district-heating network
AU - Lumbreras, Mikel
AU - Martin-Escudero, Koldobika
AU - Diarce, Gonzalo
AU - Garay-Martinez, Roberto
AU - Mulero, Ruben
N1 - Publisher Copyright:
© 2021 University of Split, FESB.
PY - 2021/9/8
Y1 - 2021/9/8
N2 - 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.
AB - 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.
KW - Data-Driven Model
KW - District-Heating Networks
KW - Heating Energy Demand
KW - Pattern Recognition
KW - Unsupervised Clustering
UR - http://www.scopus.com/inward/record.url?scp=85118449964&partnerID=8YFLogxK
U2 - 10.23919/SpliTech52315.2021.9566420
DO - 10.23919/SpliTech52315.2021.9566420
M3 - Conference contribution
AN - SCOPUS:85118449964
T3 - 2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
BT - 2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
A2 - Solic, Petar
A2 - Nizetic, Sandro
A2 - Rodrigues, Joel J. P. C.
A2 - Rodrigues, Joel J.P.C.
A2 - Gonzalez-de-Artaza, Diego Lopez-de-Ipina
A2 - Perkovic, Toni
A2 - Catarinucci, Luca
A2 - Patrono, Luigi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
Y2 - 8 September 2021 through 11 September 2021
ER -
