Demand response clustering — automatically finding optimal cluster hyper-parameter values


Time series clustering methods, such as Fuzzy C-Means (FCM) noise clustering, can be efficiently used to obtain typical price-influenced load profiles (TPILPs) through the data-driven analysis and modelling of the consumption behaviour of household electricity customers in response to price signals (Demand Response, DR). However, the analysis of load time series with cluster methods presupposes that the user has a lot of experience in selecting good cluster hyper-parameter values (e.g. number of clusters or fuzzifier). The present contribution proposes a practical method to the automatic selection of optimal hyper-parameter values for DR clustering.

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems