A new methodology based on expert knowledge and data mining is proposed to obtain data-driven models that characterize household consumption profiles. These profiles are useful for electricity marketers to understand their customers’ consumption. They could then adjust their electricity purchases in the market and provide recommendations to their customers to manage their consumption. The novelty of this research work is proposing a new procedure to determine an adequate number of clusters for a clustering task. Therefore, the proposed new methodology includes this novel procedure to build the models in two phases. In the first phase, clustering algorithms are used to group the data using different numbers of clusters. For the second phase, a new procedure (k-ISAC_TLP) is proposed and used to select the most appropriate number of clusters. This methodology allows the inclusion of domain information. In the case of household electricity consumption, where only groups with a significant number are relevant as long as the error is small, it allows the use of metrics like the mean absolute error and the number of observations (daily electricity consumption profiles). According to experts, the results achieved in two real datasets (from Spain and Ireland), with millions of observations support the methodology and reveal novel knowledge. In both cases, two and a half million observations have been analyzed and around twenty electricity consumption profiles have been detected. The methodology is easily extensible to problems of any domain where clustering algorithms are applicable. A software solution has been implemented and made freely available.
