Analysis of Customer Energy Consumption Patterns using an Online Fuzzy Clustering Technique

Currently, there is a high rate of generation of new information about the Energy Consumption of customers. It is important the traceability of its consumption pattern evolution to determine in real-time the services of a smart energy management system. This paper analyses the evolution of the Energy Consumption Pattern of customers using the Learning Algorithm for Multivariable Data Analysis (LAMDA). LAMDA is a fuzzy approach for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class/cluster, through the contributions of all its descriptors. LAMDA can create new classes/clusters after the training stage (online learning). If an individual does not have enough similarity to the preexisting classes/clusters, it is evaluated with respect to a threshold called the Non-Informative Class (NIC) to define if it is a new class/cluster. Particularly, the algorithm of the LAMDA family used in this paper is LAMDA-RD (Robust Distance). In the paper is analyzed the patterns of the initial grouping of the data, as well as, the patterns through their evolution (traceability). For the analysis of the patterns different metrics are considers: Calinski- Harabasz Index and Silhouette Score.