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A critical element of assessing a building's suitability for Demand Side Response (DSR) is understanding its turndown potential to ensure that DSR participation will be financially viable. While research has been undertaken on site level DSR estimation methods, there is currently no research that compares the outcomes of these methods. This paper compares four non-domestic energy estimation methods used for understanding the DSR potential of electrical appliances in a building to provide insights about uncertainty levels based on input requirements. Each method is deployed to estimate the DSR potential of HVAC chiller assets at two UK hotels over two years. The results show the methods have a range of error levels from the highest Mean Average Percentage Error (MAPE) of 159% to the lowest MAPE of 39%. The input requirements followed a general trend of more complex informational inputs resulting in lower error values. The outcomes of this research enable users to make informed decisions in selecting DSR estimation methods based on information availability and acceptable estimation error levels. [ABSTRACT FROM AUTHOR]

[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]