Event‐based non‐intrusive load identification algorithm for residential loads combined with underdetermined decomposition and characteristic filtering

Wu, Xin; Han, Xiao; Liang, Kai

doi:10.1049/iet-gtd.2018.6125

Cited by 23 publications

(15 citation statements)

References 32 publications

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“…Load events often occurred by turning on or off the load, thus making electrical feature change. Generally, active power is the most significant since the load events happened [26]. e associated evolution curve seems like a step jump, indicating that the load event occurs.…”

Section: Load Event Detection and Feature Extractionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi‐LSTM Algorithm

Zhou

2021

Complexity

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Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances.

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Section: Load Event Detection and Feature Extractionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi‐LSTM Algorithm

Zhou

2021

Complexity

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“…Furthermore, the summary of results of [76] was updated by incorporating very recent results found in the literature utilizing deep learning. However in the latest published deep learning approaches many researchers started utilizing databases with even lower sampling frequency and longer monitoring duration (e.g., AMPds [39] or UK-DALE [77]) as in [41,42,44,78], or utilizing different accuracy metrics (e.g., normalized RMSE in [45]) making direct comparison impossible. The results are tabulated in Table 6.…”

Section: Devicementioning

confidence: 99%

“…Furthermore, cutting edge technology in machine learning has led to a number of recently proposed in the literature deep learning approaches using big datasets, like the Almanac of Minutely Power Dataset (AMPds) [39]. Methodologies using Convolutional Neural Networks (CNNs) [40][41][42], Recurrent Neural Networks (RNNs) [43,44] and Long Short-Term Memory (LSTM) architectures [44,45], denoising autoencoders (dAEs) [46], and Gated Recurrent Units (GRUs) [40] can be found in the bibliography. Furthermore, additional questions regarding consumer privacy and real-time capability arise with the high frequent measurements of energy consumption, and have been discussed in [47,48] for security relevant issues and in [17] and [49] for low cost disaggregation and real-time capability.…”

Section: Introductionmentioning

confidence: 99%

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

2020

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A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

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“…In order to directly obtain the operating state of loads, modified event detection methods are applied for recent eventbased approaches [18][19][20][21][22]. For an event detection algorithm, the intuitive idea is to determine transient events by using a trigger threshold in detecting power signals [18].…”

Section: Introductionmentioning

confidence: 99%

“…For an event detection algorithm, the intuitive idea is to determine transient events by using a trigger threshold in detecting power signals [18]. To obtain the load waveform from the aggregate signal, a two-step iterative shrinkage threshold algorithm in the high-frequency domain was presented in [19]. Shi et al [20] introduced a hybrid similar time window algorithm to perform demand prediction in a lower-resolution dataset.…”

Section: Introductionmentioning

confidence: 99%

State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids

Yao

Zhou

et al. 2021

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Integrating the nonintrusive load monitoring (NILM) technology into smart meters poses challenges in demand-side management (DSM) of the smart grid when capturing detailed power information and stochastic consumption behaviours, due to the difficulties in accurately detecting load operation states in real household environments with the limited information available. In this paper, a state characteristic clustering (SCC) approach is presented for promoting the performance of event detection in NILM, which makes full use of multidimensional characteristic information. After identifying different types of state domains in an established multidimensional characteristic space, we design a sliding window difference search method (SWDS) to extract their initial clustering centre. Meanwhile, the mean-shift updating and iterating procedures are conducted to find the potential terminal stable state according to the probability density function. The above control strategy considers the transient events and stable states in a time-series dataset simultaneously, which thus allows the exact state of complex events to be obtained in a fluctuating environment. Moreover, a multisegment computing scheme is applied for fast computing in the state characteristic clustering process. Experiments of three different cases on both our real household dataset and REDD public dataset are provided to reveal the higher performance of the proposed SCC approach over the existing related methods.

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Event‐based non‐intrusive load identification algorithm for residential loads combined with underdetermined decomposition and characteristic filtering

Cited by 23 publications

References 32 publications

A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi‐LSTM Algorithm

A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi‐LSTM Algorithm

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids

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