Using streaming data and Apache Flink to infer energy consumption

Abstract

This paper entails the technical details of an approach to the challenge presented by the DEBS 2020 committee [5], regarding Non-Intrusive Load Monitoring (NILM) and its relevance in the area of data streaming. Our project highlights how the open source project Apache Flink can provide an efficient solution for processing large data-sets. Furthermore, we implement a version of DBSCAN, a data clustering algorithm, and we present an effective approach for handling out of order events in a data stream. We observe that our approach strikes a balance between optimization, usability, and accuracy with room for future work. We propose a complete solution that is capable of detecting appliance power events and energy consumption by using a stream of voltage and current data.