Support vector machine for classification of households' heating type using load curves

Paper i 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.