Data Mining (DM) techniques have been applied and explored in the electricity sector over the past decades. Such methods include the characterization of the typical load profiles (TLP) of the electricity consumers, electricity consumption and production forecasting (also including wind speed forecasting techniques, for example), and the definition of new electricity tariff options. Thus, this paper presents the results of several case studies where DM techniques were implemented to support decision-makers and the development of extracting knowledge from databases. This paper presents the outcomes of using DM methods in several authors’ works, such as consumption characterization (clustering and classification), formulation of new electricity tariff schemes, wind speed forecasting, and the definition of zonal prices in the power transmission grid.