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- research-articleNovember 2024
ESATED: Leveraging Extra-weak Supervision with Auxiliary Task for Enhanced Non-intrusiveness in Energy Disaggregation
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 8, Issue 4Article No.: 194, Pages 1–32https://doi.org/10.1145/3699729Non-intrusive load monitoring (NILM) is crucial to smart grid, which enables applications such as energy conservation and human activity recognition. As a typical task of NILM, energy disaggregation is to decompose total power consumption into appliance-...
- short-paperOctober 2024
Investigating Domain Bias in NILM
BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 333–336https://doi.org/10.1145/3671127.3699532Enhancing household energy efficiency is crucial, and Non-intrusive Load Monitoring (NILM) offers a valuable solution by giving consumers insights into their energy use without individual device monitoring. However, the deployment of NILM models in new ...
- research-articleSeptember 2023
Combining Smart Speaker and Smart Meter to Infer Your Residential Power Usage by Self-supervised Cross-modal Learning
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 7, Issue 3Article No.: 144, Pages 1–26https://doi.org/10.1145/3610905Energy disaggregation is a key enabling technology for residential power usage monitoring, which benefits various applications such as carbon emission monitoring and human activity recognition. However, existing methods are difficult to balance the ...
- posterJune 2023
IMG-NILM: A Deep learning NILM approach using energy heatmaps
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingPages 1151–1153https://doi.org/10.1145/3555776.3578604Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load monitoring) is low-cost, easy to ...
- research-articleDecember 2022
LightNILM: lightweight neural network methods for non-intrusive load monitoring
BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 383–387https://doi.org/10.1145/3563357.3566152The aim of non-intrusive load monitoring (NILM) is to infer the energy consumed by the appliances in a house given only the total power consumption. Recently, literature have shown that deep neural networks are the state-of-the-art approaches for ...
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- research-articleDecember 2022
Identifying impactful devices on disaggregation performance
BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 358–362https://doi.org/10.1145/3563357.3566150One of the key barriers to widespread deployment of disaggregation algorithms is the difficulty that these algorithms have in real-world environments containing many devices. While a greater number of devices inevitably results in "noisier" aggregate ...
- research-articleDecember 2022
Benefits of three-phase metering for load disaggregation
BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 393–397https://doi.org/10.1145/3563357.3566149With the ever increasing pace of introduction of energy intensive devices and services, such as electric vehicle (EV) charging and heat pumps, the transition to smart metering for three-phase electric installations for nationwide smart meter roll-outs ...
- research-articleJuly 2022
A First Approach using Graph Neural Networks on Non-Intrusive-Load-Monitoring
PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive EnvironmentsPages 601–607https://doi.org/10.1145/3529190.3534722Non-Intrusive Load Monitoring (NILM), equally referred as energy disaggregation, aims to identify the individual power of each appliance by relying exclusively on the aggregated household signal. Within this paper we propose a new seq2seq approach ...
- posterNovember 2021
User privacy leakages from federated learning in NILM applications
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 212–213https://doi.org/10.1145/3486611.3492222Non-intrusive load monitoring (NILM) is a technology that estimates the energy consumed by each appliance in the building from the main electricity meter reading only. Federal Learning (FL) is increasingly employed to construct a distributed learning ...
- short-paperNovember 2021
Neural network approaches and dataset parser for NILM toolkit
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 172–175https://doi.org/10.1145/3486611.3486652Non-intrusive load monitoring (NILM) involves separating the household aggregate energy consumption into constituent appliances. In 2014, a toolkit called NILMTK was released towards making NILM reproducible. Subsequently, in 2019, an improved version ...
- research-articleJanuary 2021
Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects
International Journal of Bio-Inspired Computation (IJBIC), Volume 17, Issue 4Pages 215–226https://doi.org/10.1504/ijbic.2021.116548Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation ...
- research-articleNovember 2020
Bayesian model of electrical heating disaggregation
NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load MonitoringPages 25–29https://doi.org/10.1145/3427771.3427848Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents ≈35% of electricity consumption with ≈40% (INSEE) of households using electrical heating. The number of ...
- short-paperNovember 2020
Compressive Non-Intrusive Load Monitoring
BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 290–293https://doi.org/10.1145/3408308.3427613In non-intrusive load monitoring (NILM), an increase in sampling frequency translates to capturing unique signal features during transient states, which, in turn, can improve disaggregation accuracy. Smart meters are capable of sampling at a high ...
- short-paperJune 2020
Long-term recurrent convolutional networks for non-intrusive load monitoring
PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive EnvironmentsArticle No.: 64, Pages 1–4https://doi.org/10.1145/3389189.3397995Identifying load measurements and monitoring the power consumption in a household is a key factor to help energy waste. Non-intrusive load monitoring is a process where machine learning and signal processing algorithms meet the need for energy ...
- demonstrationNovember 2019
A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK
- Nipun Batra,
- Rithwik Kukunuri,
- Ayush Pandey,
- Raktim Malakar,
- Rajat Kumar,
- Odysseas Krystalakos,
- Mingjun Zhong,
- Paulo Meira,
- Oliver Parson
BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 358–359https://doi.org/10.1145/3360322.3360999Non-intrusive load monitoring (NILM) or energy disaggregation involves separating the household energy measured at the aggregate level into constituent appliances. The NILM toolkit (NILMTK) was introduced in 2014 towards making NILM research ...
- research-articleNovember 2019
Towards reproducible state-of-the-art energy disaggregation
- Nipun Batra,
- Rithwik Kukunuri,
- Ayush Pandey,
- Raktim Malakar,
- Rajat Kumar,
- Odysseas Krystalakos,
- Mingjun Zhong,
- Paulo Meira,
- Oliver Parson
BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 193–202https://doi.org/10.1145/3360322.3360844Non-intrusive load monitoring (NILM) or energy disaggregation is the task of separating the household energy measured at the aggregate level into constituent appliances. In 2014, the NILM toolkit (NILMTK) was introduced in an effort towards making NILM ...
- research-articleJanuary 2019
Multi-scale residual network for energy disaggregation
International Journal of Sensor Networks (IJSNET), Volume 30, Issue 3Pages 172–183https://doi.org/10.1504/ijsnet.2019.100220Energy disaggregation technology is a key technology to realise real-time non-intrusive load monitoring (NILM). Current energy disaggregation methods use the same scale to extract features from the sequence, which makes part of the local features lost, ...
- research-articleNovember 2018
Disaggregating solar generation behind individual meters in real time
BuildSys '18: Proceedings of the 5th Conference on Systems for Built EnvironmentsPages 43–52https://doi.org/10.1145/3276774.3276776Real-time photovoltaic (PV) generation information is crucial for distribution system operations such as switching, state-estimation, and voltage management. However, most behind-the-meter solar installations are not monitored. Typically, the only ...
- research-articleJuly 2018
Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks
SETN '18: Proceedings of the 10th Hellenic Conference on Artificial IntelligenceArticle No.: 7, Pages 1–6https://doi.org/10.1145/3200947.3201011Energy disaggregation is the process of extracting the power consumptions of multiple appliances from the total consumption signal of a building. Artificial Neural Networks (ANN) have been very popular for this task in the last decade. In this paper we ...
- research-articleFebruary 2018
Modelling and power estimation of continuously varying residential loads using a quantized continuous-state hidden markov model
ICMLSC '18: Proceedings of the 2nd International Conference on Machine Learning and Soft ComputingPages 180–184https://doi.org/10.1145/3184066.3184096Hidden Markov Models (HMMs) and their extensions have broad useful applications in several fields. Energy disaggregation, or non-intrusive load monitoring (NILM), is the process of analyzing and decomposing the total aggregate energy consumption of a ...