An Instrumental High-Frequency Smart Meter with Embedded Energy Disaggregation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel smart meter prototype that performs high-frequency energy disaggregation locally using deep learning. This edge-based approach eliminates the need for cloud data transmission, improving efficiency and privacy for smart energy management.
Area Of Science
- Electrical Engineering
- Computer Science
- Energy Systems
Background
- Current smart meters often rely on low sampling rates and cloud-based processing for energy disaggregation.
- Transmitting high-frequency data from meters to the cloud presents challenges in bandwidth, latency, and privacy.
Purpose Of The Study
- To develop and evaluate a prototype smart meter capable of local, high-frequency energy disaggregation using embedded deep learning.
- To assess the impact of sampling frequency on model accuracy and edge device performance.
- To introduce novel metrics for quantifying non-intrusive load monitoring (NILM) efficiency on edge devices.
Main Methods
- Designed a smart meter prototype with a custom signal conditioning circuit and an embedded board.
- Implemented a deep learning model for energy disaggregation directly on the edge device.
- Evaluated the prototype's accuracy, power consumption, throughput, and latency across six different embedded hardware platforms.
- Introduced and applied three hardware-aware performance metrics for NILM efficiency.
Main Results
- The prototype successfully performed energy disaggregation locally at a high sampling frequency (15 kHz).
- Analysis revealed the trade-offs between sampling frequency, model accuracy, and edge device power consumption.
- Benchmarking across platforms provided insights into latency and throughput variations.
- Novel metrics offered a standardized way to evaluate NILM edge device performance.
Conclusions
- Local, high-frequency energy disaggregation on smart meters is feasible and offers advantages over cloud-based approaches.
- The developed architecture enables compact and energy-efficient NILM-enabled edge meters.
- The hardware-aware metrics provide a valuable framework for future development and comparison of NILM edge devices.
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