Contextual Pooling for Multiple-Instance Learning-based Non-Intrusive Load Monitoring

Overview of non-intrusive load monitoring and identification techniques

Non-intrusive load monitoring (NILM) is to estimate individual appliance’s power consumption from aggregated smart meter data, which is useful for optimized energy management and provisioning of customized services. While deep learning (DL) has achieved state-of-the-art NILM performance, it is still constrained by the dependency on large amounts of data and intensive computations on training. In this paper, we propose a pre-training approach to address the generalization of DL models for NILM. We develop a meta-learning based approach and an ensemble learning based approach, which pre-train a base model and then fine-tune it with few-short learning when applied to an unknown dataset. The models are validated with two real-world datasets and shown to achieve a superior transferability performance compared with traditional DL and transfer learning methods.

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations — which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations.

Knowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platform.

What we measure, we can improve. In accordance to this approach, Indian Institute of Science (IISc), Bangalore has developed a Micro-grid Monitoring System in the campus through the installation of Smart Meters, covering almost 250 nodes including substations, centers, departments, administration, hostels and other utilities. This will help the institute in various ways such as capacity planning, substation loading, phase imbalance correction, over-voltage monitoring, billing and so on. Smart Meters measure the power consumption at a single point in the building giving a picture of the energy consumption of the building as a whole. It is necessary to also understand the scenario of the constituent loads at the point where the smart meter is installed so that ways could be found to reduce consumption. Personalised, concise and reliable feedback providing appliance level breakdown of energy consumption in the premises is the key in implementing energy efficiency programs. Taking this into consideration the area of Non Intrusive Load Monitoring (NILM) was explored. In NILM the aggregate smart meter data is separated into constituent loads by machine learning techniques. The NILM system is trained through previous data sets and then the algorithm will disaggregate the total power into individual appliances based on its experience. A benchmark NILM algorithm called Factorial Hidden Markov Model was used for proper load disaggregation. Finally an attempt was made to develop a Smartphone app to visualize results and bring the data to the people.

In this paper, a new method of non intrusive load monitoring (NILM) feature extraction is proposed.The method is based on the analysis and identification of the load transient state and steady state characteristics of the load in the power supply and power outages using the back propagation neural network (BP).The current signal through current sensor is detected in the main switchboard NILM, compared with detecting active power, reactive power and harmonic components of power signals and load the instantaneous change of the state traditional NILM, this new method is more convenient and reduces the amount of computation.The new NILM method integrates artificial intelligence identification technology and load current acquisition technolog.

The use of electrical energy in remote areas must be carried out efficiently. This study proposed a load monitoring method in remote areas. This study developed a Non-Intrusive Load Monitoring (NILM) based on Data-Flow Programming (DFP) by applying a bagging decision tree algorithm to conduct load disaggregation. This study built the DFP and the Graphical User Interface (GUI) in LabVIEW connected to power sensor ADE9153A on Arduino UNO via serial communication. This experiment was conducted on the LabVIEW 2020 running on an Intel i5 2400-3.1 GHz CPU, 16 GB RAM, and 64-bit operating system computer. This study produced a good performance of NILM with 0.9617 of accuracy and 0.9728 of f1-score. The proposed method of the NILM process was suitable for electricity in remote areas because the DFP used in the algorithm is easy to understand, easy to operate, and inexpensive to build. Finally, the NILM technique can improve the efficiency of used electrical energy in remote areas. By applying NILM, the operator can determine the priority of which devices should be ON or OFF at a particular time as needed. In addition, the NILM can contribute to the balancing of small and weak microgrids in scenarios of high renewable energy penetration.

This paper brings the application of non-intrusive load monitoring (NILM) into demand response (DR). NILM is usually applied to identify the major loads in buildings, which is very promising in meeting the load monitoring requirements of demand response. Unlike the traditional approach of NILM in energy auditing, a new NILM system for DR is established based on a comprehensive analysis on the requirement of demand response. The new system is designed from both hardware and software aspects with a more practical load space and a more explicit measuring criteria. The ultimate goal of this paper is to pave the road for the future researchers to work in NILM for demand response.

Non-Intrusive load monitoring (NILM) is an emerging technology for extracting potent information from a consumer's electric load profile. The NILM techniques are gaining popularity among researchers as they reduce the requirement for external hardware for load monitoring. These techniques require only the smart meter's data, thus reducing the need for other measuring and sensing devices. In this paper, the research status of non-intrusive load monitoring has been reviewed. The utilisation of NILM techniques for event detection, load monitoring and energy disaggregation has been discussed in this paper. Furthermore, the research gap and future aspects are discussed. This paper has a great utility factor for upcoming researchers interested in NILM techniques.

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Challenges for real-world applications of nonintrusive load monitoring and opportunities for machine learning approaches

The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.

Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.

Although a diversity of approaches towards Non-Intrusive Load Monitoring (NILM) are now available, complementary studies must be carried out in order to improve load modeling and consequently increase load recognition accuracy. This paper presents a study on active power appliances' modeling when only low-frequency granularities are considered. The approach analyzes the distribution of power changes in finite-state loads using an effective feature transform to stabilize power variance. Subtractive clustering method is utilized for detection of power transition recurrences. It allows identifying most likely power changes presented in individual load profiles using a prior knowledge of power uncertainties. The principal aim of this paper is to create a probabilistic device-specific model of household resistive loads, which can be used in event-based load recognition. The results using real data prove the efficacy of the proposed method.

With the advancement of smart residential buildings, houseowners are increasingly interested to monitor the connected electrical loads in view of energy conservation and load management. Smart grid technology in the distribution system includes cutting-edge load monitoring applications, permitting efficient use of electrical energy. Nonintrusive load monitoring (NILM) technique is such a tool to recognize the consumption of energy for individual electrical loads in a residential system at a solitary point of measurement. In this article, an improved NILM method is proposed using a shunt passive filter installed at the source end of residential building with the help of current harmonic signature analysis. A fuzzy rule-based intelligent identification method is employed in order to detect and monitor different electrical loads considering various load features at the utility side. The major contribution of the proposed technique is the improvement in identification accuracy of household loads with multiple appliances. The connected filter additionally restricts the injected harmonics generated by the loads at the source end. The usage of the fuzzy technique also reduces the processor memory requirement than conventional machine learning techniques. Simulations backed by experimental results validate the viability of the proposed methodology.