Affiliations: [a] LIAAD – INESC Porto L.A. Rua de Ceuta, 118 - 6 andar, 4050-190 Porto, Portugal | [b] Faculty of Sciences of the University of Porto, Rua do Campo Alegre, 1021/1055, 4169-007 Porto, Portugal | [c] Faculty of Economics of the University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal
Abstract: Sensors distributed all around electrical-power distribution networks produce streams of data at high-speed. From a data mining perspective, this sensor network problem is characterized by a large number of variables (sensors), producing a continuous flow of data, in a dynamic non-stationary environment. Companies make decisions to buy or sell energy based on load profiles and forecast. In this work we analyze the most relevant data mining problems and issues: continuously learning clusters and predictive models, model adaptation in large domains, and change detection and adaptation. The goal is to continuously maintain a clustering model, defining profiles, and a predictive model able to incorporate new information at the speed data arrives, detecting changes and adapting the decision models to the most recent information. We present experimental results in a large real-world scenario, illustrating the advantages of the continuous learning and its competitiveness against Wavelets based prediction. We also propose a light electrical load visualization system which enhances the ability to inspect forecast results in mobile devices.
