Support vector machine for classification of households' heating type using load curves
Paper in proceeding, 2023

The distribution system operator lacks the knowledge of the heating system used by their customers to make sound grid planning decisions. Energy declaration from buildings and the large-scale rollout of smart meters provides an excellent opportunity to classify the heating system used. This paper proposes a machine-learning-based approach using a support vector machine (SVM) with daily load curves (mean and standard deviation of consumption) extracted from smart meter measurements. Three heating types are analysed: district heating, exhaust air heat pump, and direct electric heating. The performance was compared among the classifiers using daily load curves extracted over one year, for each month, each week, and each day of the year. The highest average accuracy of 92.6% was obtained for the SVM classifier using daily load curves extracted for each week of a year as features. Furthermore, the classifier showed a higher performance than using an ensemble of SVM or random forest classifiers (90.6%/90.5%) proposed in the literature. Lastly, an error analysis of the misclassification was carried out, including building characteristics and geographical analysis.

random forests

electric heating

district heating

support vector machines

smart meters

error analysis

learning (artificial intelligence)

pattern classification

heat pumps

building management systems

power engineering computing

Author

Kristoffer Fürst

Chalmers, Electrical Engineering, Electric Power Engineering

Peiyuan Chen

Chalmers, Electrical Engineering, Electric Power Engineering

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

IET Conference Proceedings

27324494 (eISSN)

Vol. 2023 6 3884-3888
978-1-83953-855-1 (ISBN)

27th International Conference on Electricity Distribution (CIRED 2023)
Rome, Italy,

Subject Categories

Energy Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

Areas of Advance

Energy

DOI

10.1049/icp.2023.0547

More information

Latest update

7/22/2024