Journal articles: 'Non-intrusive load management'

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Wu, Sheng, and Kwok L. Lo. "Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic." Processes 8, no. 11 (2020): 1385. http://dx.doi.org/10.3390/pr8111385.

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Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

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Okazawa, Kazuki, Naoya Kaneko, Dafang Zhao, et al. "Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring." Energies 17, no. 9 (2024): 2012. http://dx.doi.org/10.3390/en17092012.

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Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

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Zhou, Mengran, Shuai Shao, Xu Wang, Ziwei Zhu, and Feng Hu. "Deep Learning-Based Non-Intrusive Commercial Load Monitoring." Sensors 22, no. 14 (2022): 5250. http://dx.doi.org/10.3390/s22145250.

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Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.

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He, Nian, Dengfeng Liu, Zhichen Zhang, Zhiquan Lin, Tiesong Zhao, and Yiwen Xu. "Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management." Sensors 24, no. 10 (2024): 3109. http://dx.doi.org/10.3390/s24103109.

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State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

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Massidda, Luca, and Marino Marrocu. "A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation." Sensors 22, no. 12 (2022): 4481. http://dx.doi.org/10.3390/s22124481.

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Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

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Azizi, Elnaz, Mohammad T. H. Beheshti, and Sadegh Bolouki. "Event Matching Classification Method for Non-Intrusive Load Monitoring." Sustainability 13, no. 2 (2021): 693. http://dx.doi.org/10.3390/su13020693.

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Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

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Zhang, Xu, Jun Zhou, Chunguang Lu, Lei Song, Fanyu Meng, and Xianbo Wang. "Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering." Energies 17, no. 17 (2024): 4303. http://dx.doi.org/10.3390/en17174303.

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Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.

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Kuzmin, P. S. "NON-INTRUSIVE LOAD MONITORING: IMPLEMENTATION EFFECTS AND DISTRIBUTION PROSPECTS." Strategic decisions and risk management 10, no. 4 (2020): 306–19. http://dx.doi.org/10.17747/2618-947x-2019-4-306-319.

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The digital transition in the electric power industry is a promising goal for the development of the industry. In recent years, a wide range of technologies has been introduced into various types of activities of energy companies, including significant attention being paid to technologies that implement demand-side management, transferring consumers from the passive category to active consumers, and also opening up new opportunities in energy management. Non-intrusive load monitoring technology is of significant interest for both electricity suppliers and consumers in the USA and EEC countries, however, a Russian-language study is being carried out for the first time.The purpose of the study is to consider the concept of non-intrusive load monitoring, to formulate and systematize the effects for electric power industry entities and consumers of electricity from the introduction of technology.A review of literary sources is carried out, the most cited articles on this topic are analyzed. To calculate the propagation rate of non-intrusive load monitoring, the Bass innovation diffusion model was used. The model allows to perform an assessment based on data on similar products and has established itself as sufficiently effective for predicting the distribution of durable goods, the actual information for which has not yet been collected.For the first time, a classification of effects arising from the introduction of technology is proposed. The paper obtained a range of effects for households, energy companies, business and government. The calculation of the technology distribution rate showed that without the use of technological corridors and the systematic introduction by energy companies, the peak of adoption can be reached by 8 years from the start of implementation.Non-intrusive load monitoring allows you to get a wide range of data in order to further optimize energy consumption, increase the efficiency of enterprises, monitor the operation of equipment. There are great opportunities in the commercialization of collected data.

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Mukhtaruddin, Azharudin, Fakroul Ridzuan Hashim, Mat Kamil Awang, Husin Mamat, and Hafizi Zakaria. "Development of site-specific non-intrusive load monitoring for maximum demand control." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1814–24. https://doi.org/10.11591/ijeecs.v23.i3.pp1814-1824.

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Demand-side load management (DSM) requires greater role-play by endusers. To lower the investment for this load management concept, nonintrusive load management (NILM) was introduced as the solution. However, most of the mathematical techniques used in NILM are complex. This may hinder users from actively take part in the energy management effort. This paper explores the possibilities of applying change point detection techniques with help of differentiation and application of filters. These filters were selected strictly based on site-specific conditions. As part of the NILM implementation, a new and practical technique was developed for this paper. It was found that the developed technique, despite its simplicity it can identify the electrical equipment which added the significant load demand. The performance of the technique was found to be satisfactory as compared to results reported by other researchers.

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Liu, Siqi, Zhiyuan Xie, and Zhengwei Hu. "Research on Distributed Smart Home Energy Management Strategies Based on Non-Intrusive Load Monitoring (NILM)." Electronics 14, no. 9 (2025): 1719. https://doi.org/10.3390/electronics14091719.

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Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems. Simultaneously, it enhances energy autonomy, lowers carbon emissions, and promotes sustainable low-carbon lifestyles. By coordinating demand response programs with flexible load scheduling strategies, this approach effectively reduces peak loads and improves grid stability, thereby advancing smart grid development. This paper investigates the optimized scheduling problem in smart home energy management systems, focusing on achieving integrated optimization of multiple factors, including load balancing, cost control, carbon emission reduction, user comfort, and demand response. Considering the diverse load characteristics of residential energy systems, we propose a novel optimization framework incorporating dynamic pricing mechanisms and intelligent scheduling algorithms, which is rigorously validated through simulation experiments. Results demonstrate that the proposed scheduling strategy successfully balances economic efficiency, load management, and environmental sustainability while maintaining acceptable user comfort levels—providing a comprehensive solution for intelligent home energy management systems.

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Bucci, Giovanni, Fabrizio Ciancetta, Edoardo Fiorucci, Simone Mari, and Andrea Fioravanti. "Measurements for non-intrusive load monitoring through machine learning approaches." ACTA IMEKO 10, no. 4 (2021): 90. http://dx.doi.org/10.21014/acta_imeko.v10i4.1184.

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The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.

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Lee, Jonathan T., Sean Anderson, Claudio Vergara, and Duncan S. Callaway. "Non-Intrusive Load Management Under Forecast Uncertainty in Energy Constrained Microgrids." Electric Power Systems Research 190 (January 2021): 106632. http://dx.doi.org/10.1016/j.epsr.2020.106632.

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Mukhtaruddin, Azharudin, Fakroul Ridzuan Hashim, Mat Kamil Awang, Husin Mamat, and Hafizi Zakaria. "Development of site-specific non-intrusive load monitoring for maximum demand control." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1814. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1814-1824.

Full text

Abstract:

Demand-side load management (DSM) requires greater role-play by end-users. To lower the investment for this load management concept, non-intrusive load management (NILM) was introduced as the solution. However, most of the mathematical techniques used in NILM are complex. This may hinder users from actively take part in the energy management effort. This paper explores the possibilities of applying change point detection techniques with help of differentiation and application of filters. These filters were selected strictly based on site-specific conditions. As part of the NILM implementation, a new and practical technique was developed for this paper. It was found that the developed technique, despite its simplicity it can identify the electrical equipment which added the significant load demand. The performance of the technique was found to be satisfactory as compared to results reported by other researchers.

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Mari, Simone, Giovanni Bucci, Fabrizio Ciancetta, Edoardo Fiorucci, and Andrea Fioravanti. "A Review of Non-Intrusive Load Monitoring Applications in Industrial and Residential Contexts." Energies 15, no. 23 (2022): 9011. http://dx.doi.org/10.3390/en15239011.

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Load monitoring systems make it possible to obtain information on the status of the various loads powered by an electrical system. The term “electrical load” indicates any device or circuit that absorbs energy from the system to which it is connected, and which therefore influences electrical quantities such as power, voltage, and current. These monitoring systems, designed for applications related to energy efficiency, can also be used in other applications. This article analyzes in detail how the information derived from Non-Intrusive Load Monitoring (NILM) systems can be used in order to create Energy Management Systems (EMS), Demand Response (DR), anomaly detection, maintenance, and Ambient Assisted Living (AAL).

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Kianpoor, Nasrin, Bjarte Hoff, and Trond Østrem. "Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring." Sensors 23, no. 4 (2023): 1992. http://dx.doi.org/10.3390/s23041992.

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Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%.

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Shabbir, Noman, Kristina Vassiljeva, Hossein Nourollahi Hokmabad, Oleksandr Husev, Eduard Petlenkov, and Juri Belikov. "Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring." Electronics 13, no. 8 (2024): 1420. http://dx.doi.org/10.3390/electronics13081420.

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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Guo, Xiaochao, Chao Wang, Tao Wu, Ruiheng Li, Houyi Zhu, and Huaiqing Zhang. "Detecting the novel appliance in non-intrusive load monitoring." Applied Energy 343 (August 2023): 121193. http://dx.doi.org/10.1016/j.apenergy.2023.121193.

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Jrhilifa, Ismael, Hamid Ouadi, Abdelilah Jilbab, Saad Gheouany, Nada Mounir, and Saida El Bakali. "VMD-GRU Based Non-Intrusive Load Monitoring For Home Energy Management System." IFAC-PapersOnLine 58, no. 13 (2024): 176–81. http://dx.doi.org/10.1016/j.ifacol.2024.07.479.

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Hosseini, Sayed Saeed, Kodjo Agbossou, Sousso Kelouwani, and Alben Cardenas. "Non-intrusive load monitoring through home energy management systems: A comprehensive review." Renewable and Sustainable Energy Reviews 79 (November 2017): 1266–74. http://dx.doi.org/10.1016/j.rser.2017.05.096.

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Hariyanto, Nurman, Dian Anggraini, and Ary Setijadi Prihatmanto. "Smart Home Electric Energy Management Using Non-Intrusive Appliance Load Monitoring (NILM)." Journal of Software Engineering, Information and Communication Technology (SEICT) 2, no. 2 (2021): 77–84. http://dx.doi.org/10.17509/seict.v2i1.34572.

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Reducing the use of electrical energy in everyday life can be done with the awareness of the user. Awareness of using electrical energy can be done by providing information about the use of electricity itself. In developing a smart home with energy management systems or other commercial electronic devices, a tool that can measure or sort electricity usage in buildings and households is needed based on current and voltage units. Measuring and sorting what is meant is separating the total power consumption used as a load of a specific device that can be used by applying the Non-Intrusive Load Monitoring (NILM) technique known as Energy Disaggregation. The results are shown by NILM using the IoT concept data will be sent to the server via the internet using Message Queuing Telemetry Transport (MQTT). The data is processed and given to the user in the form of measurement results for each electronic device connected to the measuring device. From these results, the system can separate the energy from the refrigerator and air conditioner from the total energy consumed at one time. This step is one way to make energy efficient, that an energy management system with iot concept is built.

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Janpirom, Chaovarit, Wachirawictch Nilsook, and Aekkarat Suksukont. "Development of hybrid CNN-LSTM for non-intrusive load monitoring." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 575–82. https://doi.org/10.53894/ijirss.v8i2.5244.

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Non-Intrusive Load Monitoring (NILM) is a technique used to distinguish the energy consumption of individual electrical devices from aggregated energy consumption data without requiring additional sensors on each device. This technology plays a crucial role in efficient energy management, reducing energy costs, and supporting the development of smart buildings. This research focuses on developing a hybrid deep learning network to enhance NILM efficiency by combining convolutional neural networks with long short-term memory networks. This combination enables the analysis of complex electrical power signals, improving the accuracy of device classification, reducing prediction errors, and enhancing learning efficiency from diverse data. The proposed method is trained using electrical appliance interaction data in three configurations: 2-appliance, 3-appliance, and 4-appliance interactions. Experimental results demonstrate training accuracies of 98.59%, 98.59%, and 93.09%, respectively, while the highest testing accuracies are 98.59%, 95.61%, and 92.94%. These results highlight the potential for further advancements in NILM technology, enabling more efficient energy monitoring systems and promoting sustainable energy use in the future.

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Dinesh, Chinthaka, Shirantha Welikala, Yasitha Liyanage, Mervyn Parakrama B. Ekanayake, Roshan Indika Godaliyadda, and Janaka Ekanayake. "Non-intrusive load monitoring under residential solar power influx." Applied Energy 205 (November 2017): 1068–80. http://dx.doi.org/10.1016/j.apenergy.2017.08.094.

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Temneanu, Marinel, and Andrei Ardeleanu. "Non-Intrusive Hybrid Energy Monitoring System." Advanced Materials Research 837 (November 2013): 495–99. http://dx.doi.org/10.4028/www.scientific.net/amr.837.495.

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When the optimization of the manufacturing units is considered, the management usually focus on equipment costs and technology parameters. However, being the rising price of the energy, the energy efficiency has become a sensitive issue. Detailed energy related information should be collected in order to understand the consumption profile of each relevant machine/process and identify potential energy savings. Data gathering is time-consuming and very expensive since individual metering devices have to be used for each consumer. To overcome this inconvenient, several solution have been reported. All of them are making use of a single metering device (monitoring the overall energy consumption) and some Load Signature Identification algorithms, used to disaggregate the overall energy between the identified consumers. Their relatively low detection rate (about 80-90%) hindered the spread of LSI-based architectures. In this context, a new hybrid architecture is proposed in this paper, together with it's hybrid LSI algorithm. Several test performed in an electronic prototyping facility result in a detection rate close to 100%.

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Sundas and Intisar Ali Sajjad. "Non-Intrusive Load Identification of Residential Appliances Using Improved Dictionary Learning Technique." Journal of Artificial Intelligence and Computing 1, no. 2 (2023): 30–36. http://dx.doi.org/10.57041/xm49bx49.

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With the advent of time, the demand for power in the residential sector is increasing. Along with supply-side management, the demand side is also used to balance electricity and supply demand. To apply different demand-side management techniques, the energy disaggregation on metered data is used to retrieve information related to available demand. Non-intrusive load monitoring is a technique that separates the total power consumption into appliance loads with minimum invasion of privacy. Non-intrusive load monitoring covers the methods of Stochastic finite state machines, Neural Networks and Sparse Coding. Developing an efficient algorithm for NILM is a key challenge in maximizing energy conservation. Recently, a new deep learning technique called dictionary learning has been developed for energy disaggregation. Smart meters provide the whole house data, and the Dictionary technique is trained to predict an appliance's power or ON/OFF based on its power consumption. This research proposes the event-based dictionary learning technique, which can disaggregate multiple appliances through orthogonal matching pursuit (OMP) and kernel-singular value decomposition (K-SVD). The sparse matrix is predicted through OMP, and K-SVD predicts the dictionary matrix. The training, testing and validation are done on the ECO dataset. The results of this research are noticeable and show the validity of the proposed methodology for energy disaggregation.

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Xiao, Ziwei, Jiaqi Yuan, Wenjie Gang, Chong Zhang, and Xinhua Xu. "A NILM method for cooling load disaggregation based on artificial neural network." E3S Web of Conferences 111 (2019): 05020. http://dx.doi.org/10.1051/e3sconf/201911105020.

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The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

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Liu, Yu, Qianyun Shi, Yan Wang, Xin Zhao, Shan Gao, and Xueliang Huang. "An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity." Sensors 21, no. 22 (2021): 7750. http://dx.doi.org/10.3390/s21227750.

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Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

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Su, Lihong, Xiuxia Hao, and Shukun Dong. "An unsupervised non-intrusive load monitoring method for HVAC systems based on the MSTL method." Journal of Physics: Conference Series 3001, no. 1 (2025): 012015. https://doi.org/10.1088/1742-6596/3001/1/012015.

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Abstract The HVAC load identification helps quantify energy flexibility potential, enabling demand response and efficient energy management because the HVAC load constitutes nearly half of the commercial building load. The HVAC load disaggregated by non-intrusive load monitoring from aggregated electricity data is an economical and easy-to-implement solution. In this study, a multiple seasonal-trend decomposition using loess (MSTL) method is proposed for load decomposition based on historical load data. Considering the multiple periodic features of the load data, the MSTL based on weekly and monthly features includes two basic multiplicative STL models, which are connected as a parallel framework. The aggregated load can be decomposed by the MSTL into several components, including seasonal, trend, and residual components. Then the HVAC load can be constructed by these components. The proposed method is validated with historical electricity data from 5 buildings. Results show that the proposed MSTL can disaggregate the HVAC load effectively with a CVRMSE of 14.3%, a WAPE of 11.28%, and an NRMSE of 2.75%. Compared to the traditional STL method which only considers weekly features, the MSTL method achieves reductions in CVRMSE, NRMSE, and WAPE by up to 10.83%, 2.08%, and 6.89%, respectively. The proposed method offers a resilient unsupervised load disaggregation solution to obtain the HVAC load for better energy management.

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Yan, Lei, Wei Tian, Jiayu Han, and Zuy Li. "Event-driven two-stage solution to non-intrusive load monitoring." Applied Energy 311 (April 2022): 118627. http://dx.doi.org/10.1016/j.apenergy.2022.118627.

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Yao, Li, Wei Liu, Chunguang Lu, Tao Xiao, and Yilong Li. "User-side energy-saving potential assessment method based on non-intrusive load identification technology." Journal of Physics: Conference Series 2378, no. 1 (2022): 012044. http://dx.doi.org/10.1088/1742-6596/2378/1/012044.

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Abstract With the development of smart grid, smart meters have been widely used, and the Non-Intrusive Load Monitoring (NILM) technology for sensing and identifying electricity load has gradually matured. Aiming at the problem that the characteristics of industrial users’ load status are few and the gap between different industries is large, this paper uses the random forest algorithm to build a load identification model, and at the same time builds an energy-saving potential evaluation index system including three dimensions of economy, technology and management. Based on the load identification results, users can evaluate the energy-saving potential, and output the use strategies and power consumption suggestions of electric equipment.

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Ramadan, Rawda, Qi Huang, Amr S. Zalhaf, et al. "Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things." Smart Cities 7, no. 4 (2024): 1907–35. http://dx.doi.org/10.3390/smartcities7040075.

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Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.

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Enríquez, R., M. J. Jiménez, and M. R. Heras. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration." Applied Energy 191 (April 2017): 44–54. http://dx.doi.org/10.1016/j.apenergy.2017.01.050.

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Devarapalli, Hari Prasad, V. S. S. Siva Sarma Dhanikonda, and Sitarama Brahmam Gunturi. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion." Energies 13, no. 18 (2020): 4628. http://dx.doi.org/10.3390/en13184628.

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Demand Response (DR) plays a vital role in a smart grid, helping consumers plan their usage patterns and optimize electricity consumption and also reduce harmonic pollution in a distribution grid without compromising on their needs. The first step of DR is the disaggregation of loads and identifying them individually. The literature suggests that this is accomplished through electric features. Present-day households are using modern power electronic-based nonlinear loads such as LED (Light Emitting Diode) lamps, electronic regulators and digital controllers to reduce the electricity consumption. Furthermore, usage of SMPS (Switched-Mode Power Supply) for computing and mobile phone chargers is increasing in every home. These nonlinear loads, while reducing electricity consumption, also introduce harmonic pollution into the distribution grid. This article presents a deterministic approach to the non-intrusive identification of load patterns using percentage Total Harmonic Distortion (THD) for DR management from a Power Quality perspective. The percentage THD of various combinations of loads is estimated by enhanced dual-spectrum line interpolated FFT (Fast Fourier Transform) with a four-term minimal side-lobe window using a LabVIEW-based hardware setup in real time. The results demonstrate that percentage THD identifies a different combination of loads effectively and advocates alternate load combinations for recommending to the consumer to reduce harmonic pollution in the distribution grid.

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Du, Shengli, Mingchao Li, Shuai Han, Jonathan Shi, and Heng Li. "Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data." Energies 12, no. 6 (2019): 992. http://dx.doi.org/10.3390/en12060992.

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The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

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Luan, Wenpeng, Zun Wei, Bo Liu, and Yixin Yu. "Non-intrusive power waveform modeling and identification of air conditioning load." Applied Energy 324 (October 2022): 119755. http://dx.doi.org/10.1016/j.apenergy.2022.119755.

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Li, Chuyi, Kedi Zheng, Hongye Guo, and Qixin Chen. "A mixed-integer programming approach for industrial non-intrusive load monitoring." Applied Energy 330 (January 2023): 120295. http://dx.doi.org/10.1016/j.apenergy.2022.120295.

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Moradzadeh, Arash, Sahar Zakeri, Waleed A. Oraibi, Behnam Mohammadi-Ivatloo, Zulkurnain Abdul-Malek, and Reza Ghorbani. "Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures." Sustainability 14, no. 22 (2022): 14898. http://dx.doi.org/10.3390/su142214898.

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Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

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Liu, Chao, Adedotun Akintayo, Zhanhong Jiang, Gregor P. Henze, and Soumik Sarkar. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network." Applied Energy 211 (February 2018): 1106–22. http://dx.doi.org/10.1016/j.apenergy.2017.12.026.

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Welikala, Shirantha, Neelanga Thelasingha, Muhammed Akram, Parakrama B. Ekanayake, Roshan I. Godaliyadda, and Janaka B. Ekanayake. "Implementation of a robust real-time non-intrusive load monitoring solution." Applied Energy 238 (March 2019): 1519–29. http://dx.doi.org/10.1016/j.apenergy.2019.01.167.

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Chen, Yung-Yao, Ming-Hung Chen, Che-Ming Chang, Fu-Sheng Chang, and Yu-Hsiu Lin. "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." Sensors 21, no. 8 (2021): 2883. http://dx.doi.org/10.3390/s21082883.

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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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Li, Dandan, Jiangfeng Li, Xin Zeng, et al. "Transfer learning for multi-objective non-intrusive load monitoring in smart building." Applied Energy 329 (January 2023): 120223. http://dx.doi.org/10.1016/j.apenergy.2022.120223.

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Yu, Xiangqian, Xiaoming Li, Jianhua Chen, Zhiguo Wang, and Shiyuan Zhang. "Research on Nonintrusive Load Decomposition of Enterprises Based on Bidirectional LSTM." E3S Web of Conferences 233 (2021): 01070. http://dx.doi.org/10.1051/e3sconf/202123301070.

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To detect the operating condition of equipment and understand the environmental management situation of enterprises in real-time, this paper studies the non-intrusive load decomposition of enterprises based on bidirectional LSTM. In this paper, we first obtain the load characteristic parameters of different equipment in different states, and then obtain the electrical power measured from the main power meter of decontamination equipment in TOP-5 through the softmax layer of bidirectional LSTM, and then change the softmax layer to decompose the load data from the main power meter.

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Zhao, Bochao, Minxiang Ye, Lina Stankovic, and Vladimir Stankovic. "Non-intrusive load disaggregation solutions for very low-rate smart meter data." Applied Energy 268 (June 2020): 114949. http://dx.doi.org/10.1016/j.apenergy.2020.114949.

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He, Gengsheng, Yu Huang, Ying Zhang, et al. "Hybrid Transformer–Convolutional Neural Network Approach for Non-Intrusive Load Analysis in Industrial Processes." Energies 18, no. 10 (2025): 2464. https://doi.org/10.3390/en18102464.

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With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial environments, such as cement plants. This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. An innovative time–application attention mechanism was integrated to effectively model long-term temporal dependencies and the collaborative operational relationships between industrial devices. Additionally, global constraints—including consistency, smoothness, and sparsity—were introduced into the loss function to ensure power conservation, reduce noise, and achieve precise zero-power predictions for inactive equipment. The proposed method was validated on real-world power consumption data collected from a cement production facility. Experimental results indicate that the proposed method significantly outperforms traditional NILM approaches with average improvements of 4.98%, 3.70%, and 4.38% in terms of accuracy, recall, and F1-score, respectively. These findings underscore its superior robustness in noisy conditions and under device fault conditions, further affirming its applicability and potential for deployment in industrial settings.

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Hu, Minzheng, Shengyu Tao, Hongtao Fan, Xinran Li, Yaojie Sun, and Jie Sun. "Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation." Sensors 21, no. 16 (2021): 5366. http://dx.doi.org/10.3390/s21165366.

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To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

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Ono, Tetsushi, Aya Hagishima, and Jun Tanimoto. "Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning." Sustainability 14, no. 22 (2022): 14863. http://dx.doi.org/10.3390/su142214863.

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Understanding occupants’ behaviours (OBs) of heating and cooling use in dwellings is essential for effectively promoting occupants’ behavioural change for energy saving and achieving efficient demand response operation. Thus, intensive research has been conducted on data collection, statistical analysis, and modelling of OBs. However, the majority of smart metres currently deployed worldwide monitor only the total household consumption rather than appliance-level load. Therefore, estimating the turn-on/off state of specific home appliances from the measured household total electricity referred to as non-intrusive load monitoring (NILM), has gained research attention. However, the current NILM methods overlook the specific features of inverter-controlled heat pumps (IHPs) used for space heating/cooling; thus, they are unsuitable for detecting OBs. This study presents a rule-based method for identifying the occupants’ intended operation states of IHPs based on a statistical analysis of load data monitored at 423 dwellings. This method detects the state of IHPs by subtracting the power of sequential-operation appliances other than IHPs from the total household power. Three time-series characteristics, including the durations of power-on/off states and power differences between power-off/on states, were used for this purpose. The performance of the proposed method was validated, indicating an F-score of 0.834.

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Sun, Ruichen, Kun Dong, and Jianfeng Zhao. "DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model." Sensors 23, no. 7 (2023): 3540. http://dx.doi.org/10.3390/s23073540.

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Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis of household energy consumption without requiring individual metering of every appliance, and has the capability to provide valuable insights into energy usage behavior, facilitate energy conservation, and optimize load management. Currently, deep learning models have been widely adopted as state-of-the-art approaches for NILM. In this study, we introduce DiffNILM, a novel energy disaggregation framework that utilizes diffusion probabilistic models to distinguish power consumption patterns of individual appliances from aggregated power. Starting from a random Gaussian noise, the target waveform is iteratively reconstructed via a sampler conditioned on the total active power and encoded temporal features. The proposed method is evaluated on two public datasets, REDD and UKDALE. The results demonstrated that DiffNILM outperforms baseline models on several key metrics on both datasets and shows a remarkable ability to effectively recreate complex load signatures. The study highlights the potential of diffusion models to advance the field of NILM and presents a promising approach for future energy disaggregation research.

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Çimen, Halil, Najmeh Bazmohammadi, Abderezak Lashab, Yacine Terriche, Juan C. Vasquez, and Josep M. Guerrero. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring." Applied Energy 307 (February 2022): 118136. http://dx.doi.org/10.1016/j.apenergy.2021.118136.

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Chang, Hsueh-Hsien. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses." Energies 5, no. 11 (2012): 4569–89. http://dx.doi.org/10.3390/en5114569.

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Gopinath, R., Mukesh Kumar, C. Prakash Chandra Joshua, and Kota Srinivas. "Energy management using non-intrusive load monitoring techniques – State-of-the-art and future research directions." Sustainable Cities and Society 62 (November 2020): 102411. http://dx.doi.org/10.1016/j.scs.2020.102411.

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Esmaeili Shayan, M. "Application of IoT and Non-Intrusive Load Monitoring Techniques in Microgrid Energy Management and Monitoring Systems." Iranica Journal of Energy and Environment 15, no. 3 (2024): 287–93. http://dx.doi.org/10.5829/ijee.2024.15.03.07.

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Journal articles on the topic 'Non-intrusive load management'

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