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[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]