Comparing clustering approaches for smart meter time series: Investigating the influence of dataset properties on performance

The widespread adoption of smart meters for monitoring energy consumption has generated vast quantities of high-resolution time series data which remain underutilised. While clustering has emerged as a fundamental tool for mining smart meter time series (SMTS) data, selecting appropriate clustering methods remains challenging despite numerous comparative studies. These studies often rely on problematic methodologies and consider a limited scope of methods, frequently overlooking compelling methods from the broader time series clustering literature. Consequently, they struggle to provide dependable guidance for practitioners designing their own clustering approaches.