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On time series representations for multi-label NILM
Neural Computing and Applications, 2020Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons.
Christoforos Nalmpantis, Dimitris Vrakas
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2019
Non Intrusive Load Monitoring is the field that encompasses energy disaggregation and appliance detection. In recent years, Deep Neural Networks have improved the classification performance, using the standard data representation that most datasets provide; that being low-frequency or high-frequency data. In this paper, we explore the NILM problem from
Lamprini Kyrkou +2 more
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Non Intrusive Load Monitoring is the field that encompasses energy disaggregation and appliance detection. In recent years, Deep Neural Networks have improved the classification performance, using the standard data representation that most datasets provide; that being low-frequency or high-frequency data. In this paper, we explore the NILM problem from
Lamprini Kyrkou +2 more
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Data Augmentation Strategies for High-Frequency NILM Datasets
IEEE Transactions on Instrumentation and Measurement, 2023The Nonintrusive Load Monitoring (NILM) process aims to monitor the different appliances connected to a power grid. It accomplishes this by analyzing one or multiple signals measured at the main breaker level of the power grid. In order for it return a response with high fidelity, it needs to be trained and tested using data pools of individual and ...
Nour, Mohamad +3 more
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NILM in an Industrial Setting: A Load Characterization and Algorithm Evaluation
2016 IEEE International Conference on Smart Computing (SMARTCOMP), 2016Industrial buildings are responsible for a large share of the worldwide electricity consumption. Disaggregated information about electricity consumption enables decision- making and feedback tools to reduce and optimize the electricity consumption. In industrial settings, electrical load comes from a variety of equipment and machinery which can be ...
Holmegaard, Emil +1 more
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States and Power Consumption Estimation for NILM
2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019This paper presents Nonintrusive Load Monitoring (NILM) for electrical home appliances network which consists of a known set of devices. Hidden Markov Model (HMM) is used for system modeling. The proposed method enhances determining and defining all states for each device.
Neveen M. Hussein +2 more
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Different Sampling Rates on Neural NILM Energy Disaggregation
2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), 2019Energy disaggregation is a process to estimate an appliance power usage by providing only a household power reading. One of the techniques used in the energy disaggregation is to use neural network. However, when using neural network in the energy disaggregation, it can be time consuming for an online or real-time applications.
Kitisak Osathanunkul +1 more
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Appliance Anomaly Detection as NILM Extention
2023 IEEE PES 15th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2023Madhawa Herath +3 more
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D5.1 Herit4ages Energy Router and NILM
This deliverable outlines the design, simulation, and testing of the Smart Energy Router (SER) and a new high-frequency Non-Intrusive Load Monitoring (NILM) method for energy-efficient retrofitting of cultural heritage buildings. The SER integrates PV (including an aesthetic heritage-compatible option), battery storage, and the home grid, with ...Martins, Joao +2 more
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Challenges Associated with Standardization of NILM Methodology
2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022Barkha Parkash +7 more
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A Contrastive NILM Approach for Appliance Detection
2023 7th International Conference on Internet of Things and Applications (IoT), 2023Arya Ebrahimi +3 more
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