Electricity load forecast using data streams techniques

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 nonstationary 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 maintain in real-time 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.