Non-intrusive Load Monitoring Research Articles

Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning

Background: The electricity demand is continuously increasing. However, various institutions, enterprises, and individuals exhibit many irregularities in their electricity usage, leading to significant wastage of electricity. To achieve effective energy management, researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non- Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM, they utilize a large amount of computational resources and require high sampling rates from devices, yet the results are still unsatisfactory. Therefore, it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies. Objective: To improve the accuracy of load identification with low sampling frequency data, this paper proposes a typical scenario load identification method based on feature fusion and transfer learning. Methods: This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM, effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training, not only is the accuracy improved, but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition, selecting typical scenario loads can monitor loads in a targeted manner, reduce the waste of computing resources caused by irrelevant loads, and more effectively guide electricity usage strategies. Results: The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios, improving accuracy by 3% to 7% compared to other models. Conclusion: The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.

Non-intrusive identification of building loads using EDCA-ShuffleNetV2 with fused feature visualization

Abstract Non-intrusive load monitoring (NILM) enables monitoring of appliance operation status and energy consumption without additional meters, providing innovative data support for energy management. However, current NILM systems still face challenges such as insufficient load feature information and low load identification accuracy. To utilize the load feature information and improve the accuracy of load identification more effectively, this paper first selects loads with high energy consumption and high usage frequency as the research object, makes full use of the load feature information through feature fusion, and converts the fused typical load data into an image through the Gramian summation angular field, and further carries out the identification of loads that still have similarity after feature fusion. Secondly, this paper improves the ShuffleNetV2 model structure by embedding an efficient dual-channel attention (EDCA) module in the basic feature extraction module of ShuffleNetV2 to enhance it to extract adequate load feature information. A residual structure is also introduced to mitigate the information loss and gradient disappearance issues of the model. Finally, the superiority and effectiveness of the fused load features and the proposed model in load identification and typical scenario applications are validated using the iAWE and AMPDS datasets.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches

An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches

Wavelet-based convolutional neural network for non-intrusive load monitoring of next generation shipboard Power Systems

In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform signal processing and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the NILM model. The current signals are first processed through a discrete wavelet transform to create a coefficient vector that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN architecture model to monitor the load statuses in real time. The results of case studies show that the developed NILM model in comparison with benchmark methods can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks, load uncertainty, and noisy inputs.

Load Recognition With Few-Shot Transfer Learning Based on Meta-Learning and Relational Network in Non-Intrusive Load Monitoring

Load Recognition With Few-Shot Transfer Learning Based on Meta-Learning and Relational Network in Non-Intrusive Load Monitoring

Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security

Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDF

A training-free non-intrusive air conditioning load monitoring method based on fuzzy comprehensive evaluation

Air conditioner (AC) is a grid friendly load, monitoring its operating status plays a key role in evaluating user side potential for participation in demand response. With continuous innovations in AC technology, the AC operation characteristics have shown increasing complexity, which poses significant challenges for non-intrusive AC load monitoring. Furthermore, obtaining labeled training data for target scenarios is often challenging, which hinders deployment of many Non-Intrusive Load Monitoring (NILM) methods in practice. In this regard, this article proposes a training-free non-intrusive AC load monitoring method based on fuzzy comprehensive evaluation (FCE), which is very easy to deploy and has good robustness. Firstly, considering various influence factors, a FCE model is constructed based on common knowledge about AC. This model simulates the manual labeling process to locate AC operation periods. Secondly, an AC power estimation method based on weighted filtering is established, in which weighted filter is used to eliminate the non-AC load components that may overlap during AC operation periods. Specifically, an optimization model is constructed to make the cumulative power of the AC events in its working cycle approach zero, with the filter weight vector as the objective variable, in which a heuristic greedy algorithm is used to find the optimal filter weight vector. When multiple feasible solutions with similar objective function values are derived in the solving process, the probability of load events appearing in AC and non-AC operation periods are compared. The final solution is selected, taking into account the likelihood of each feasible solution corresponding to a load event sequence belonging to AC load. The testing results on the PecanStreet dataset and multiple real-world measured datasets prove the effectiveness of the proposed method in monitoring complex ACs with poor waveform consistency.

Optimising energy efficiency enhancing NILM through high- resolution data analytics

This study introduces a novel approach to enhancing energy efficiency by building a high-resolution dataset for Non-Intrusive Load Monitoring, addressing the challenges of monitoring a wide range of devices. To achieve optimal energy efficiency, it is essential to have advanced monitoring of electrical variables. In this context, the second version of openZmeter is presented, which supports up to 4 devices, each capable of 160 measurements, including voltage, current and power harmonics on each channel. To carry out this purpose, the Non-Intrusive Load Monitoring Toolkit is adapted for the new openZmeter v2. The main objective of this study is to offer a new pragmatic approach to generating artificial intelligence models to optimise energy use, analysing the results through an exhaustive experimental analysis under various casuistry and conditions. Numerous comparative studies are provided using classical disaggregation algorithms with different requirements. Conclusively, the research emphasises the transformative potential of artificial intelligence for energy efficiency strategies, offering insights for scholars and practitioners.

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Non-intrusive load monitoring based on equipment operation state

Non-intrusive load monitoring based on equipment operation state

Smart Non-Intrusive Appliance Load-Monitoring System Based on Phase Diagram Analysis

Much of today’s power grid was designed and built using technologies and organizational principles developed decades ago. The lack of energy resources and classic power networks are the main causes of the development of the smart grid to efficiently use energy resources, with stable and safe operation. In such a network, one of the fundamental priorities is provided by non-intrusive appliance load monitoring (NIALM) in order to analyze, recognize and determine the electricity consumption of each consumer. In this paper, we propose a new smart system approach for the characterization of the appliance load signature based on a data-driven method, namely the phase diagram. Our aim is to use the non-intrusive load monitoring of appliances in order to recognize different types of consumers that can exist within a smart building.

Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things

Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things

Sparse coding-based transfer learning for energy disaggregation

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a technique that predicts the consumption levels of individual appliances from only the main signal in the building. Various methods have been proposed to solve this problem, including sparse coding (SC), which offers great advantages due to its ability to capture complex patterns in data. However, a challenging aspect of NILM is that data containing appliance-level information is scarce. Moreover, the houses that the models are tested on might be from a different population than the training data, thus resulting in a domain shift. Therefore, we need to develop approaches that are adapted to training data scarcity through the use of transfer learning (TL). In this paper, we test several TL approaches on SC models with the aim of discriminative energy disaggregation (DD). We propose four TL SC models: “Transfer SC+DD”, “SC source, DD target”, “Transfer Deep SC” and “Transfer Variational SC”. We compare these models with 4 models that do not apply domain adaptation. We show that TL-based models achieve similar and sometimes better results than the regular models. We also show that further training on only the aggregate signal of some target domain houses improves the performance of a SC model trained only on the source.

Research on Privacy Issues in Smart Metering System: An Improved TCN-Based NILM Attack Method and Practical DRL-Based Rechargeable Battery Assisted Privacy Preserving Method

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

Home Energy Management System for a Residential Building in Arctic Climate of Norway Using Non-Intrusive Load Monitoring and Deep Learning

Home Energy Management System for a Residential Building in Arctic Climate of Norway Using Non-Intrusive Load Monitoring and Deep Learning

Semi-supervised learning with flexible threshold for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can obtain fine-grained power consumption information for individual appliances within the user without installing additional hardware sensors. With the rapid development of the deep learning model, many methods have been utilized to address NILM problems and have achieved enhanced appliance identification performance. However, supervised learning models require a substantial volume of annotated data to function effectively, which is time-consuming, laborious, and difficult to implement in real scenarios. In this paper, we propose a novel semi-supervised learning method that combines consistency regularization and pseudo-labels to help identification of appliances with limited labeled data and an abundance of unlabeled data. In addition, given the different learning difficulties of various appliance categories, for example, feature learning is more difficult for multi-state appliances than two-state appliances, the thresholds employed for different appliances are adjusted in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be delivered. Experiments have been conducted on publicly available datasets, and the results indicate that the proposed method attains superior appliance identification performance compared to cutting-edge methods.

Comparing four machine learning algorithms for household non-intrusive load monitoring

The combination of Machine Learning (ML), smart energy meters, and availability of household appliance energy profile data has opened new opportunities for Non-Intrusive Load Monitoring (NILM). However, the number of options makes it challenging in selecting optimal combinations for different energy applications, which requires studies to examine their trade-offs.This paper contributes one such study that investigated four established ML approaches – K Nearest Neighbour (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) – and their performance in classifying appliance events from Alternating Current (AC) and Root Mean Square (RMS) energy data where the sampling frequency and training dataset set size was varied (10 Hz–1 kHz and 50–2000 examples per class, respectively). The computational expense during training, testing and storage was also assessed and evaluated with reference to real-world applications.The CNN classifier trained on AC data at 500 Hz and 11,000 examples gave the best F1-score 0.989 followed by the KNN classifier 0.940. The storage size required by the CNN models was 3̃MB, which is very close to fitting on cost-effective embedded system microcontrollers. This would prevent high-rate data needing to be sent to the cloud as analysis could be performed on edge computing Internet-of-Things (IoT) devices.

Non-intrusive load monitoring based on MoCo_v2, time series self-supervised learning

Traditional non-intrusive load monitoring (NILM) methods rely on massive historical labeled data. However, due to the privacy and high labeling cost of datasets, their generality and feasibility are limited, with poor performance in identifying devices with multiple states or similar features. To address this issue, this paper proposes an SSCL-LM framework based on temporal convolutional network (TCN), integrating contrastive self-supervised learning into NILM. Initially, through contrastive self-supervised pre-training, the framework learns well-represented load temporal characteristics from abundant unlabeled 1D power data. Then a small amount of labeled data is used to fine-tune the classifier to learn load categories represented by different temporal characteristics. Finally, test data is inputted into the model for load identification. Validating on the REDD dataset, the results demonstrate that with only 30% labeled data for fine-tuning, the proposed method achieves a 4.04% higher F1 score compared to traditional supervised methods. Moreover, utilizing only 1D power features, this method exhibits superior identification performance for devices with multiple states or similar features. Furthermore, this method enables performance transfer among loads of the same device with different parameters across different households, verifying its strong generalization and practicality.