Traditional Non-intrusive Load Monitoring Research Articles

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

A New NILM System Based on the SFRA Technique and Machine Learning.

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.

A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural Network

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets.

An adaptive lightweight seq2subseq model for non‐intrusive load monitoring

Non-intrusive load monitoring (NILM) is an important technology for deeply mining consumers' internal electricity consumption information, which can improve the level of awareness of the load and significantly improve the demand-side management capability of the smart grid. For a long time, traditional NILM faces the challenge of huge data, long training time and low accuracy. To address this issue, this paper proposes an adaptative lightweight seq2subseq model. The model adaptively determines the optimum window length of the active power segment to extract the appliance characteristics and thus reduces the redundant data to construct the appliance-level-seq2subseq model. The proposed model significantly improves the ability to extract and utilize information. At the same time, transfer learning is used to ban the training of appliance models with similar characteristics, further reducing model complexity and training time. The experimental results based on the REDD dataset verify the method's effectiveness. Compared with the seq2seq and seq2point models, the proposed model achieved better accuracy and F1 score, up to 98.10% and 90.29%, respectively. At the same time, the proposed method is further verified on UK-DALE, which proves that the proposed method has good portability and robustness.

Non-Intrusive Adaptive Load Identification Based on Siamese Network

The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on classification models, which have several deficiencies. Firstly, a large amount of labeled data is required to train the classification model. Secondly, these algorithms cannot identify unknown devices that frequently encountered in practical application. Finally, these models have poor performance in versatility, which means they only adapt to the trained data. These shortcomings greatly influence the practicality of these NILM algorithms. To tackle these problems, this paper has proposed a non-intrusive adaptive load identification model based on the Siamese network, which uses both the V-I trajectory and active power as the load signatures. The Siamese network is utilized to calculate the similarity of the V-I trajectory, and the load identification is realized by matching the signature with the feature library. Through adding new features to the feature library dynamically, the identification of unknown load can be realized. In addition, the Siamese network is a typical network for few-shot learning, thus the proposed model can be trained with a small number of samples to achieve ideal recognition effect. At last, the validity and versatility of the model are verified in PLAID dataset and COOLL dataset.

Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids' success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NILM have not dealt with practical problems when applied to power grid, i.e., ① limited communication for slow-change systems; ② requirement of low-cost hardware at the users' side; and ③ inconvenience to adapt to new households. Therefore, a novel NILM algorithm based on biology-inspired spiking neural network (SNN) has been developed to overcome the existing challenges. To provide intelligence in NILM, the developed SNN features an unsupervised learning rule, i.e., spike-time dependent plasticity (STDP), which only requires the user to label one instance for each appliance while adapting to a new household. To upgrade the feasibility in NILM, the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor (RC) circuit. In addition, a distributed computing system has been designed that divides the SNN into two parts, i.e., smart outlets and local servers. Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM, the high-frequency data can be easily compressed as the spike times, and are sent to the local server with limited communication capability, whereas it is unable to handle the traditional NILM. Finally, a series of experiments are conducted using a benchmark public dataset. Meanwhile, the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.

PUMPNET: a deep learning approach to pump operation detection

Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

Enhancing neural non-intrusive load monitoring with generative adversarial networks

The application of Deep Learning methodologies to Non-Intrusive Load Monitoring (NILM) gave rise to a new family of Neural NILM approaches which increasingly outperform traditional NILM approaches. In this extended abstract describing our ongoing research, we analyze recent Neural NILM approaches and our findings imply that these approaches have difficulties in generating valid, reasonably-shaped appliance load profiles. We propose to enhance Neural NILM approaches with appliance load sequence generators trained with a Generative Adversarial Network to mitigate the described problem. The preliminary results of our experiments with Generative Adversarial Networks show the potential of the approach, albeit there is no strong evidence yet that this approach outperforms the examined end-to-end-trained Neural NILM approaches. In the progress of our investigations, we generalize energy-based NILM performance metrics and establish the complete classification confusion matrix based on the estimated energy in appliance load profiles. This enables the adaption of all known classification scores to their energy-based counterparts.

Non-intrusive Load Disaggregation Based on Kernel Density Estimation

Aiming at the problem of high cost and difficult implementation of high frequency non-intrusive load decomposition method, this paper proposes a new method based on kernel density estimation(KDE) for low frequency NILM (Non-intrusive load monitoring). The method establishes power reference model of electricity load in different working conditions and appliance’s possible combinations first, then probability distribution is calculated as appliances features by kernel density estimation. After that, target power data is divided by step changes, whose distributions will be compared with reference models, and the most similar reference model will be chosen as the decomposed consequence. The proposed approach was tested with data from the GREEND public data set, it showed better performance in terms of energy disaggregation accuracy compared with many traditional NILM approaches. Our results show good performance which can achieve more than 93% accuracy in simulation.

Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.