Bolt

Lange, Henning; Bergés, Mario

doi:10.1145/2993422.2993581

Cited by 16 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher sampling rates can capture subtle changes (high frequency ripples), which are useful for appliance identification 5 , 23–25 . Capturing the voltage and current waveforms allows energy disaggregation algorithms such as BOLT 6 to extract patterns directly from the raw measurement data. To the best of our knowledge, only the datasets in (refs 10–12 ) provide aggregated sampling rates above 10kSps.…”

Section: Background and Summarymentioning

confidence: 99%

BLOND, a building-level office environment dataset of typical electrical appliances

Kriechbaumer

Jacobsen

2018

Sci Data

View full text Add to dashboard Cite

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.

show abstract

Section: Background and Summarymentioning

confidence: 99%

BLOND, a building-level office environment dataset of typical electrical appliances

Kriechbaumer

Jacobsen

2018

Sci Data

View full text Add to dashboard Cite

show abstract

Anomaly‐detection‐based learning for real‐time data processing in non‐intrusive load monitoring

Chen,

Dong,

2024

Energy Conversion and Econom

View full text Add to dashboard Cite

A power system can be regarded as a cyber‐physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non‐intrusive load monitoring (NILM), an emerging smart‐grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection‐based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber‐attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection‐based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real‐time assessment of power‐ loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

show abstract

Dyna-Bolt: Domain Adaptive Binary Factorization Of Current Waveforms For Energy Disaggregation

Chen

Liu

Lange

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Bolt

Cited by 16 publications

References 9 publications

BLOND, a building-level office environment dataset of typical electrical appliances

BLOND, a building-level office environment dataset of typical electrical appliances

Anomaly‐detection‐based learning for real‐time data processing in non‐intrusive load monitoring

Dyna-Bolt: Domain Adaptive Binary Factorization Of Current Waveforms For Energy Disaggregation

Contact Info

Product

Resources

About