Residential Electrical Load Monitoring and Modeling – State of the Art and Future Trends for Smart Homes and Grids

Yuan, Xinmei; Han, Peng; Duan, Yao; Alden, Rosemary E.; Rallabandi, Vandana; Ionel, Dan M.

doi:10.1080/15325008.2020.1834019

Cited by 19 publications

(26 citation statements)

References 97 publications

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“…On the one hand, in [5], the authors explained that the development of power electronics significantly improves the accuracy and flexibility of power control, but greatly affects the applicability of NILM methods. Power converters not only allow the power of appliances to be continuously adjusted, but also eliminate harmonics and compensate the reactive power.…”

Section: Related Workmentioning

confidence: 99%

“…These solutions feed on the energy usages of appliances that are measured separately by submeters. Therefore, appliance-level load data needs to be directly labeled [5]. Among the most common ILM applications are understanding local and global energy consumption, evaluation and simulation of NILM environments, human activity recognition, and appliance localization.…”

Section: Intrusive Load Monitoringmentioning

confidence: 99%

“…Continuously variable devices are considered extremely difficult to recognize since they can exhibit significantly different patterns depending on their usage [4]. Based on their consumption patterns, the class of permanent consumer devices can be understood as a sub-class of ON/OFF appliances [5]. FSMs or multistate devices have a variant number of states, and so the consumption of each appliance.…”

Section: B Load Classificationmentioning

confidence: 99%

“…The appliance recognition system assigns a label that corresponds to the device or appliance connected to the sensor. Moreover, future load monitoring techniques are expected to be hybrid, combining the benefits of NILM and individual power measurement by smart plugs, smart appliances, and HEMS [5].…”

Section: Introductionmentioning

confidence: 99%

“…However, different requirements should be considered concerning data resolution, accuracy, real-time, and the number of appliances to be covered. To the best of our knowledge, there is no previous work available of ILM for appliance recognition and ADL classification due to issues of cost, installation, and communication [5], [8].…”

Section: Introductionmentioning

confidence: 99%

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IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Intrusive Load Monitoringmentioning

confidence: 99%

Section: B Load Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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Deep reinforcement learning-based network for optimized power flow in islanded DC microgrid

Pitchaimani¹,

Asokan²,

Kathiresan³

et al. 2023

Electr Eng

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Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Ouzine¹,

Marzouq²,

Bennani³

et al. 2022

E3S Web Conf.

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Reduction and conservation of electrical energy consumption in residential buildings is the main objective of Non-Intrusive Load Monitoring (NILM) techniques. NILM detects events and estimate the power consumption of individual appliances by analyzing the aggregate power consumption measured at the service entry. Indeed, our major contribution in this paper is to update research works on NILM methodologies by adding the most recent NILM methods proposed in the literature. In this paper we present an overview of NILM and energy disaggregation methods. Then, we discuss the challenges of this technique to provide useful recommendations for future research.

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Residential Electrical Load Monitoring and Modeling – State of the Art and Future Trends for Smart Homes and Grids

Cited by 19 publications

References 97 publications

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Deep reinforcement learning-based network for optimized power flow in islanded DC microgrid

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

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