DeepEdge-NILM: A case study of non-intrusive load monitoring edge device in commercial building

Gopinath, R.; Kumar, Mukesh

doi:10.1016/j.enbuild.2023.113226

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…The basic reason for that is that deploying such applications on the edge eliminates the need to transfer data between the users and a central data source, addressing the challenges tied to central data processing and privacy. The landscape of research in edge computed NILM is broad and includes different approaches, from deep learning models on edge devices [8], [9], [22]- [24] to feature extraction [25]- [27], federated learning [28] and hardware-specific optimizations such as Field-Programmable Gate Arrays (FPGAs) [29] and e-Sense device [30]. Since NILM research has mainly been traversed to deep learning techniques, there is a growing interest in works that deal with NILM inference on edge devices to be deployed as part of Home Energy Management Systems [31].…”

Section: B Dnn Compression Methods For Nilmmentioning

confidence: 99%

OPT-NILM: An Iterative Prior-to-Full-Training Pruning Approach for Cost-Effective User Side Energy Disaggregation

Athanasoulias,

Sykiotis,

Kaselimi

et al. 2024

IEEE Trans. Consumer Electron.

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Non-Intrusive Load Monitoring describes the process of analyzing the aggregate household energy consumption to infer the individual energy consumption patterns of different appliances. Although NILM research has led to substantial progress as regards the performance of deep learning models, these models require exhaustive resources for the training phase and, due to their computational demand, are not well suited for deployment on edge devices with limited resources. NILM applications on low-resource devices enhance user adoption, opening up new energy market prospects. Although there has been some work toward edge-computed NILM, the proposed compression frameworks provide a solution only for the deployment phase since they are applied to the already trained models. This study presents OPT-NILM, a novel pruning strategy to discover sub-optimal NILM neural networks before full training, which reduces computing costs for both testing and training phase, and improves disaggregation performance compared to conventional after-training pruning. OPT-NILM proposes a metric to find the appropriate pruning threshold by evenly valuing model performance and computing cost, unlike other approaches that apply compression arbitrarily. Experimental results on the UK-Dale dataset show that the OPT-NILM approach may reduce model trainable parameters by up to 95% with minimal performance loss.

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Section: B Dnn Compression Methods For Nilmmentioning

confidence: 99%

OPT-NILM: An Iterative Prior-to-Full-Training Pruning Approach for Cost-Effective User Side Energy Disaggregation

Athanasoulias,

Sykiotis,

Kaselimi

et al. 2024

IEEE Trans. Consumer Electron.

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“…However, NILM requires the installation of advanced sensors for data acquisition, and calibration for signal-device matching, and heavily relies on accurate algorithms for energy allocation. In recent years, NILM approaches have gained momentum as artificial intelligence (AI), embedded devices, and the Internet of Things (IoT) have advanced substantially [26]. In many applications, such as providing an informative breakdown of the energy use of the house, only a good estimation of the energy use for different appliances will suffice.…”

Section: State-of-the-art Techniques In End-use Load Decompositionmentioning

confidence: 99%

A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters

Sharifi,

Kouti,

Lambie

et al. 2023

Energies

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The availability of building operational data such as energy use and indoor temperature provides opportunities to enhance the traditional building energy performance calculation. Disaggregated building energy use facilitates informed decision-making to identify cost-saving measures efficiently at the individual building and building stock levels. The existing energy performance analysis techniques with measured input data in the literature are fragmented. Moreover, they frequently approach this issue with varying degrees of complexity depending on the available input data, expertise, and time. The procedure of choosing an appropriate method is often cumbersome with limited indication of the usefulness of the outcomes. This study proposes a data-driven framework for end-use load disaggregation through techniques that exploit various kinds of building consumption data. The results demonstrate the use of different techniques for varied applications. Calibrated theoretical calculation, data-driven heat loss coefficient (HLC), and energy signature curve (ESC) are among the proposed methods in the framework that facilitate individual, and urban scale energy decomposition. It is observed that different methods yield unalike outcomes, while their performance is predictable. While the HLC methods are flexible but also highly sensitive to the input parameters, the ESC needs high-frequency time series but provides stable energy decomposition. The ESC is efficient for large-scale analysis and the HLC method for detailed case-specific applications. Calibrated theoretical energy decomposition has a simple workflow and can supplement the current energy performance assessment method, although it entails sufficient input data.

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“…Thi-Thu-Huong Le et al [31] applied sequence-to-sequence long short-term memory networks to transient signals after Hilbert transformation, enhancing the differentiation in transient time and shape. Gopinath and Kumar [32] focused on identifying and disaggregating similar devices, proposing the DeepEdge-NILM model, which effectively captures the features of similar loads using geometric means. Rafiq et al [33] used mutual information to select multiple feature inputs…”

Section: Introductionmentioning

confidence: 99%

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Xu,

Jiang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

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DeepEdge-NILM: A case study of non-intrusive load monitoring edge device in commercial building

Cited by 12 publications

References 56 publications

OPT-NILM: An Iterative Prior-to-Full-Training Pruning Approach for Cost-Effective User Side Energy Disaggregation

OPT-NILM: An Iterative Prior-to-Full-Training Pruning Approach for Cost-Effective User Side Energy Disaggregation

A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

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