Non-Intrusive Load Identification Method Based on Improved KM Algorithm

Xiao, Yong; Hu, Yue; He, Hengjing; Zhou, Dongguo; Zhao, Yun; Hu, Wenshan

doi:10.1109/access.2019.2948079

Cited by 23 publications

(17 citation statements)

References 32 publications

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“…The method in [39] is based on a super-state hidden Markov model to disaggregate multi-state loads. Load event matching-based methods are proposed in [44], [45], [47] to perform load disaggregation. Moreover, several NILM algorithms were presented in [48], [49] based on deep neural networks.…”

Section: B Nilm Methodsmentioning

confidence: 99%

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

2020

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Power utility companies rely on forecasting to anticipate future consumption needs, plan power production, and schedule the selling/purchasing of power. We present a novel method to forecast the power consumption of a single house based on non-intrusive load monitoring (NILM) and affinity aggregation spectral clustering, with the idea of extending it to forecasting consumption in a larger set of houses like a microgrid. First, we use a graph to model statistical relationships between appliances. Specifically, the ON/OFF time-of-day and state duration probabilities are used to compute graph edge weights and establish statistical relationships among appliances. Then, leveraging on our previous work on NILM, we disaggregate the smart meter aggregate power profile into individual appliance power profiles. With the disaggregated individual power profiles and the corresponding ON/OFF time-of-day and state duration probabilities, we next propose a method to forecast each appliance's power profiles using affinity aggregation spectral clustering. For the proposed method, we incorporate human behaviour and environmental influence in terms of calendar and seasonal contexts in order to enhance the forecasting performance. Finally, the results of appliance-level forecasting are aggregated to perform house-level forecasting. To test our proposed forecasting method, we use four publicly available datasets and compare our method against several existing approaches such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting. Experimentally, we examine how well the proposed forecasting method can generalize appliance behaviours from one house to another. Results clearly show that our method is more accurate than existing approaches. INDEX TERMS Appliance identification, demand side management, non-intrusive load monitoring, NILM, power forecasting, smart grid, spectral clustering.

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Section: B Nilm Methodsmentioning

confidence: 99%

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

2020

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“…Considering AMI in a smart grid, smart metering technology enables the prediction of future electricity demands, which can be used by policymakers to make decisions regarding increasing electricity demands. The ongoing development and deployment of smart meters [ 21 , 22 ] has made DSM that deals with ever-increasing electricity demands feasible [ 23 , 24 ]. HEMSs that optimize residential energy consumption in response to DR signals for DSM play an important role in a smart grid.…”

Section: Introductionmentioning

confidence: 99%

“…Sensors 2021, 21, x FOR PEER REVIEW 3 of 25 electricity demands feasible [23,24]. HEMSs that optimize residential energy consumption in response to DR signals for DSM play an important role in a smart grid.…”

Section: Introductionmentioning

confidence: 99%

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Chen

Chang³

et al. 2021

Sensors

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Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

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“…ere are various methods for feature extraction [5] and load identification [6], including supervised classification methods [7], unsupervised clustering methods [8], and optimization methods [9]. Among them, the deep learning algorithms are widely used in the field of load identification [10,11].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

2021

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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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Non-Intrusive Load Identification Method Based on Improved KM Algorithm

Cited by 23 publications

References 32 publications

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

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

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