Clustering commercial and industrial load patterns for long-term energy planning

In future smart energy systems, consumers are expected to change their load patterns as they become a significant source of flexibility. To ensure reliable load profile forecasts for long-term energy planning, conventional classification approaches will not hold and more advanced solutions are required.

In this article, we propose an automatic, data-driven clustering methodology that accounts for heterogeneity in electricity consumers’ load profiles using unsupervised learning. We consider hourly load measurements from 9412 smart-meters from the commercial and industrial sector in Denmark. A wavelet transform is applied to min-max scaled load data, and the extracted wavelet coefficients are used as input to the K-means clustering algorithm.

Through cluster validation, eight clearly distinct load profiles are identified and compared to the industry classification of the cluster constituents. Finally, the flexibility potential is traced for each cluster.