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- ArticleMay 2024
GraphNILM: A Graph Neural Network for Energy Disaggregation
Advances in Knowledge Discovery and Data MiningPages 431–443https://doi.org/10.1007/978-981-97-2253-2_34AbstractNon-Intrusive Load Monitoring (NILM) remains a critical issue in both commercial and residential energy management, with a key challenge being the requirement for individual appliance-specific deep learning models. These models often disregard the ...
- research-articleFebruary 2024
Assessing the Effectiveness of Supervised and Semi-supervised NILM Approaches in an Industrial Context
CIIS '23: Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent SystemsPages 7–13https://doi.org/10.1145/3638209.3638211Non-Intrusive Load Monitoring (NILM) is a technique that aims to estimate the energy consumption and operational status of individual appliances in a building by analyzing only the aggregate power usage data. This technique plays a crucial role in demand-...
- research-articleJune 2023
Exploring of Recursive Model-based Non-Intrusive Thermal Load Monitoring for Building Cooling Load
e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy SystemsPages 120–124https://doi.org/10.1145/3599733.3600259Non-Intrusive Load Monitoring (NILM), which provides sufficient load information from the energy consumption of the entire building, has become crucial in improving the operation of energy systems. Although it can decompose overall energy consumption ...
- 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-articleMarch 2023
Smart plug for monitoring and controlling electrical devices with a wireless communication system integrated in a LoRaWAN
Expert Systems with Applications: An International Journal (EXWA), Volume 213, Issue PAhttps://doi.org/10.1016/j.eswa.2022.118976Highlights- User control and metering of network-connected devices with mobile application.
The application of Long-Range Wide-Area Network (LoRaWAN) in Internet of Things (IoT) monitoring applications has grown exponentially in recent history because of its cost-effectiveness, robustness to interference, low power ...
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- research-articleDecember 2022
Appliance recognition with combined single- and multi-label approaches
BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 388–392https://doi.org/10.1145/3563357.3566153The problem of appliance recognition is one of the most relevant issues in the field of Non-Intrusive-Load-Monitoring; its importance has led, in recent years, to the development of innovative techniques to try to solve it. The use of methods such as V-I ...
- research-articleDecember 2022
Using explainability tools to inform NILM algorithm performance: a decision tree approach
BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationPages 368–372https://doi.org/10.1145/3563357.3566148Over the years, Non-Intrusive Load Monitoring (NILM) research has focused on improving performance and more recently, generalizing over distinct datasets. However, the trustworthiness of the NILM model itself has hardly been addressed. To this end, it ...
- research-articleOctober 2022
Gridchain: an investigation of privacy for the future local distribution grid
International Journal of Information Security (IJOIS), Volume 22, Issue 1Pages 29–46https://doi.org/10.1007/s10207-022-00622-6AbstractAs part of building the smart grid, there is a massive deployment of so-called smart meters that aggregate information and communicate with the back-end office, apart from measuring properties of the local network. Detailed measurements and ...
- research-articleAugust 2022
Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
International Journal of Intelligent Systems (IJIS), Volume 37, Issue 10Pages 7124–7179https://doi.org/10.1002/int.22876AbstractSmart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. ...
- 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 ...
- research-articleJune 2022
EVSense: a robust and scalable approach to non-intrusive EV charging detection
e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy SystemsPages 307–319https://doi.org/10.1145/3538637.3538860As the number of electric vehicles (EVs) increases, large-scale residential EV charging will burden the power grid, posing problems for both planning and operations. Promptly capturing EV charging events can help mitigate this problem. However, most ...
- research-articleApril 2022
Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks
AbstractThe intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging ...
- research-articleJune 2022
Dynamic Time Series Data Reduction for NILM Appliance Identification
ICIAI '22: Proceedings of the 2022 6th International Conference on Innovation in Artificial IntelligencePages 105–111https://doi.org/10.1145/3529466.3529489Advancements in Internet of Things capabilities along with cheap & easy-to-use sensors have led to the development of many new domains, Non-Intrusive Load Monitoring being one of them. A crucial element of these technologies is appliance identification ...
- 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 ...
- short-paperJune 2021
Introducing MILM: A Hybrid Minimal-Intrusive Load Monitoring Approach: Poster
e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy SystemsPages 298–299https://doi.org/10.1145/3447555.3466578The shift towards an advanced electricity metering infrastructure has gained traction because of several smart meter roll-outs. This accelerated research in Non-Intrusive Load Monitoring techniques. These techniques highly benefit from the temporal ...
- research-articleJune 2021
Electricity Demand Activation Extraction: From Known to Unknown Signatures, Using Similarity Search
e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy SystemsPages 148–159https://doi.org/10.1145/3447555.3464865A powerful tool for reducing energy consumption is energy disaggregation (also called NILM Non-Intrusive Load Monitoring), where the goal is to disaggregate the smart meter readings of a household's total electricity consumption to the consumption of ...
- research-articleMarch 2021
A convolutional autoencoder-based approach with batch normalization for energy disaggregation
The Journal of Supercomputing (JSCO), Volume 77, Issue 3Pages 2961–2978https://doi.org/10.1007/s11227-020-03375-yAbstractNon-intrusive loading monitoring (NILM) is a load analyzing algorithm that performs the energy dis-aggregation of power load for the smart meter technology. NILM is a highly valuable application due to its cost effectiveness, but it is a very ...
- short-paperNovember 2020
BERT4NILM: A Bidirectional Transformer Model for Non-Intrusive Load Monitoring
NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load MonitoringPages 89–93https://doi.org/10.1145/3427771.3429390Non-intrusive load monitoring (NILM) based energy disaggregation is the decomposition of a system's energy into the consumption of its individual appliances. Previous work on deep learning NILM algorithms has shown great potential in the field of energy ...
- short-paperNovember 2020
Stop: Exploring Bayesian Surprise to Better Train NILM
NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load MonitoringPages 39–43https://doi.org/10.1145/3427771.3429388In Non-Intrusive Load Monitoring (NILM), as in many other machine learning problems, significant computational resources and time are spent training models using as much data as possible. This is perhaps driven by the preconception that more data leads ...
- research-articleNovember 2020
On the Relationship between Seasons of the Year and Disaggregation Performance
NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load MonitoringPages 70–74https://doi.org/10.1145/3427771.3427856This paper pursues the question of how seasons of the year affect disaggregation performance in Non-Intrusive Load Monitoring. To this end, we select the dishwasher, a common household appliance that may exhibit usage cycles depending on the user. We ...