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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia UniversityMontrealCanada

non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

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Event-Driven non-intrusive load monitoring Algorithm Based on Targeted Mining Multidimensional load Characteristics

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China Communications 2023年第5期20卷 40-56页

作者： Gang Xie Hongpeng Wang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.

关键词： non-intrusive load monitoring learning to ranking smart grid electrical characteristics

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Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques

Energy and AI

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Energy and AI 2023年第3期13卷 88-103页

作者： Marco Toledo-Orozco C.Celi F.Guartan Arturo Peralta Carlos Alvarez-Bel D.Morales Institute for Energy Engineering Universitat Politecnica de Val`enciaCamino de VeraValencia46022Spain Electrical Engineering Career Universidad Politecnica SalesianaSede Cuenca010103Ecuador Electrical Engineering Career Circular Economy Laboratory-CIITTUniversidad Catolica de CuencaSede Cuenca010107Ecuador

Technological advances,innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems.The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models,generating inefficiency in the analysis and processing of informa-tion to validate the flexibility potential that large consumers can contribute to the network operator.In this sense,the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.

关键词： Big data Combinatorial optimization Factorial hidden Markov model Machine learning non-intrusive load monitoring Time of use tariffs

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non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

能源与人工智能(英文)

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能源与人工智能(英文) 2021年第1期3卷 180-191页

作者： Attique Ur Rehman Tek Tjing Lie Brice Valls Shafiqur Rahman Tito School of Engineering Computerand Mathematical SciencesAuckland University of TechnologyNew Zealand Brice Valles Consulting New Zealand School of Professional Engineering Manukau Institute of TechnologyNew Zealand

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.

关键词： non-intrusive load monitoring load-Shed Authentication Demand Response Machine Learning Model Genetic Algorithm Energy Efficiency

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Analysis of Dynamic Appliance Flexibility Considering User Behavior via non-intrusive load monitoring and Deep User Modeling

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CSEE Journal of Power and Energy Systems 2020年第1期6卷 41-51页

作者： Shaopeng Zhai Huan Zhou Zhihua Wang Guangyu He School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 201100China Electric Power Dispatching and Communication Center State Grid Shanghai Municipal Electric Power CompanyShanghai 201200China

The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)programs.Appliance flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort level.In this paper,a dynamic appliance flexibility analysis approach using the smart meter data is presented.In the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user model.During operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal information.The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the appliance.The HEMS can choose the appliance with high flexibility to participate in the DR programs.The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.

关键词： Appliance flexibility demandresponse home energy management system non-intrusive load monitoring user behavior

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A Review of NILM Applications with Machine Learning Approaches

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Computers, Materials & Continua 2024年第5期79卷 2971-2989页

作者： Maheesha Dhashantha Silva Qi Liu School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

In recent years,non-intrusive loadmonitoring (NILM) has become an emerging approach that provides affordableenergy management solutions using aggregated load obtained from a single smart meter in the power grid.Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less ofa burden for the energy monitoring process. However, conducted research works have limitations for real-timeimplementation due to the practical issues. This paper aims to identify the contribution of ML approaches todeveloping a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,along with the research works that have been conducted in the domain. Secondly, the contribution of machinelearning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and hybridmodeling.It highlights the limitations in the applicability of ML approaches in the field. Then, the challenges in the realtimeimplementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time,cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection andnew appliance identification, and complexity of the electricity loads and real-time demand side management.Furthermore, options for selecting an approach for an efficientNILMframework are suggested. Finally, suggestionsare provided for future research directions.

关键词： non-intrusive load monitoring transfer learning machine learning feature extraction

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non-intrusive load monitoring Method Based on SAGA-FCM Algorithm

Non-intrusive Load Monitoring Method Based on SAGA-FCM Algor...

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作者： Zhou-da HU Yang FANG Wen-quan YE Yuan-xi YIN Yudean Group Guizhou Co. Ltd.

Existing non-intrusive algorithms have some deficiencies such as low recognition accuracy and the accuracy decreases significantly when the amount of monitoring data is too large. An improved fuzzy C-means algorithm（FCM） is proposed in this paper Which will make up the shortcomings of existing algorithms. In order to optimize the clustering process,the simulated annealing algorithm（SA） and genetic algorithm（GA） are introduced to fasten the process. The experimental and comparison results fully demonstrate the superiority of the new FCM in terms of recognition, robustness, stability and reliability. Furthermore, the new FCM is applied to practical household appliances. All the comparison results consistently indicate that the new FCM is highly competitive and can be used in big data monitoring environment.

关键词： non-intrusive load monitoring Delta feature extraction Genetic simulated annealing algorithm The fuzzy C-means algorithm

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Graph representation learning-based residential electricity behavior identification and energy management

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Protection and Control of Modern Power Systems 2023年第2期8卷 218-230页

作者： Xinpei Chen Tao Yu Zhenning Pan Zihao Wang Shengchun Yang School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China China Electric Power Research Institute Nanjing 210003China

It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.

关键词： Behavior correlation graph Graph reinforcement learning Home energy management system Multi-label classification non-intrusive load monitoring

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Training load monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data

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Tsinghua Science and Technology 2008年第S1期13卷 406-411页

作者： Mario Berges Ethan Goldman H. Scott Matthews Lucio Soibelman Civil and Environmental Engineering Department Carnegie Mellon University

The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the devel-opment of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.

关键词： electricity metering feedback energy conservation non-intrusive load monitoring

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