Abstract
This paper provides a tutorial on the most recent advances of event-driven metering (EDM) while indicating potential extensions to improve its performance. We have revisited the effects on signal reconstruction of (i) a fine-tuned procedure for defining power variation events, (ii) consecutive-measurements filtering that refers to the same event, (iii) spike filtering, and (iv) timeout parameter. We have illustrated via extensive numerical results that EDM can provide high-fidelity signal reconstruction while decreasing the overall number of acquired measurements to be transmitted. Its main advantage is to only store samples that are informative based on predetermined events, avoiding redundancy and decreasing the traffic offered to the underlying communication network. This tutorial highlights the key advantages of EDM and points out promising research directions.
Highlights
H OUSEHOLD electrification is widespread: nine out of ten people worldwide have access to electricity [2]
The event-driven metering (EDM) method has the potential to significantly reduce the metering data volume generated and transmitted, as far as the values falling under the threshold are discarded instead of being stored and/or sent, causing minimum impact on the quality of signal reconstruction
The main objective is to demonstrate how the number of measurements generated by each customer is kept within a given range, by using their own consumption to determine the power variation thresholds. By exploiting this knowledge in semantics-empowered EDM, we identify ways to reduce the acquired data by filtering those measurements that trigger events with low impact on the signal reconstruction process
Summary
H OUSEHOLD electrification is widespread: nine out of ten people worldwide have access to electricity [2]. The rationale behind this automated data acquisition resembles the manual method, because measurements are generated in a content-agnostic manner without exploiting the inherent attributes of metering information In principle, these measurements are sent in periodic time intervals and are defined as time-based measurements. To the best of the authors’ knowledge, this limitation has not been adequately addressed in existing related literature This contribution is a tutorial on recent improvements of EDM based on a semantics-aware approach that harnesses intrinsic contextual attributes of metering information. Range, by using their own consumption to determine the power variation thresholds By exploiting this knowledge in semantics-empowered EDM, we identify ways to reduce the acquired data by filtering those measurements that trigger events with low impact on the signal reconstruction process. TOMÉ et al.: ADVANCES IN EVENT-DRIVEN DATA ACQUISITION FOR ELECTRICITY METERING (JUNE 2021)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
