Enhancing NILM classification via robust principal component analysis dimension reduction

Dimension reduction for NILM classification based on principle component analysis

Energy management using non-intrusive load monitoring techniques – State-of-the-art and future research directions

Non-Intrusive Load Monitoring (NILM) techniques are today of great interest for several applications contexts. Indeed, the possibility of exploiting these techniques for having energy consumption data in a unique aggregated metric is very attractive not only for the cost-saving (due to the installation of a reduced number of meters) but also for catching important information coming from the field and addressed to implement predictive maintenance paradigms on the devices of interest. As matter of fact, the continued analysis (by means of the NILM techniques) of each load behavior could enable in detecting of dangerous situations or also identify incoming slow faults occurring on the loads themselves. Although the NILM techniques have been deeply analyzed in the scientific literature, some lacks are related to the analysis of the impact of the measurement uncertainty on their performance as well as to the capability of correctly identifying the loads operating against the number of loads present. In this framework, this paper proposes a preliminary sensitivity analysis aimed at verifying the impact of the meters measurement uncertainty and the number of loads involved on the performance of a well-known NILM technique, i.e. the Combinatorial Optimization (CO). To this aim, several measurement accuracies, power metrics, and the number of loads have been taken into account. The achieved results prove how the considered quantities of influence generally affect the performance of CO and can also provide useful information to the designers of the measurement system as an example in defining the best feature to be used, the maximum measurement uncertainty for a given number of loads, able to warrant the target accuracy required by the specific application.

Applications of NILM Techniques to Energy Management and Assisted Living

Overview of non-intrusive load monitoring and identification techniques

In order to enable demand response schemes for residential and industrial users, it is crucial to be able to predict and monitor each component of the total power consumption of a household or of an industrial site over time. We used the cross-validation method which is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. We exploit Non-Intrusive Load Monitoring (NILM) techniques in order to provide behavior patterns of the variables identified. This work presents a review Non-Intrusive Load Monitoring (NILM) techniques and describe the results of recognition patterns used for the identification of electrical devices. The proposed method has been validated on an experimental setting and using direct measurements of appliances consumption, proving that it allows achieving a high level of accuracy in load disaggregation.

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.

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.

Renewable energy sources are slowly but surely supplementing conventional sources of energy. These energy sources create a large number of micro-grids which become a part of the main grid. Smart meters are essential components in smart micro-grids and smart grids. Together they form a complex system which has become today an attractive area of research world-wide in view of enormous benefits of reliability and safety of power supply economically. Several studies in smart meter applications have shown that in residential buildings, energy consumption can be reduced up to 15% with better management. For this, a non-intrusive appliance load monitoring (NIALM) technique has been advocated which enables appliance identification to help better energy management. The identification is done using load signatures with appropriate algorithm. Several features of load signatures ranging from voltage and current profiles (steady states and transients) to a plethora of features such as active power (P) and reactive power (Q), power factor (pf), harmonic components (h), magnitudes of h (mh), current waveform (CW), eigen values, switching transient waveform, instantaneous admittance waveform, etc have been used in NIALM. The algorithms used vary from a `single algorithm' to `multiple algorithms', the latter used in `Committee Decision Mechanism' (CDM) in which majority of `votes' (i.e., agreement), `cast' by each of the algorithms, decides the final result of appliances identified. This scenario presents a serious difficulty with regard to choice of features and algorithms, besides the possibility of increasing the hardware and software costs of smart meters. The motivation for the study in this paper is provided by this scenario. The basic objective of this paper is to investigate the possibility of including only a limited number of features as load signature of appliances, and the use of only a single algorithm to effectively implement NIALM without any ambiguity. Results of our study so far indicate that generally only four features of P, Q, h and mh are sufficient for detection of appliances in operation; however, in some cases (~>10%), the additional feature, CW, may be needed for final identification.. The experiments conducted so far yielded unambiguous identification of appliances and the results are encouraging enough for possible use in practice for residential loads only.

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.

Non-intrusive Load Monitoring (NILM) proves effective in disaggregating energy consumption data for individual household appliances. Traditional NILM techniques rely on one-dimensional (1D) time series signal (TSS) analysis to predict the energy usage of distinct appliances. Nonetheless, these approaches have limitations in handling data with non-linear behavior and complex patterns. To address this challenge, prior studies have proposed converting 1D TSS into two-dimensional (2D) images using techniques like Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP). Recent research underscores the effectiveness of the Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), both variants of GAF, in the NILM domain. This study assesses the performance of three distinct 1D to 2D conversion approaches for energy disaggregation: GADF, GASF, and an enhanced hybrid method integrating GADF and GASF. Performance evaluation utilizes matrices, including accuracy, precision, recall, and the F1 score. The experiments are conducted on a publicly accessible real-world energy dataset encompassing different appliances. The results demonstrate that the combined GADF and GASF method introduced in this study outperforms their individual counterparts with better performance and faster convergence, particularly in the context of energy disaggregation. The findings offer valuable insights for advancing the development of more accurate and efficient NILM techniques.

Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system.

Nonintrusive load monitoring (NILM) technique tries to measure the power consumption of all appliances at the power meter, the single point where the power cable enters your home. In literature, many algorithms have been proposed to detect appliance usage in smart grid to detect how much and what appliances are in use. In this paper, we emphasis on the issue where the appliance is in use. In practice the signal noise on the power line makes it difficult to differentiate appliances when they are very close. In this paper, we propose a statistical learning based technique that can detect different appliance usage events that occurs very close within just several centimeters. The main idea is to take the sample mean of the appliance readings and apply the hypothesis testing on the sample mean to determine the decision threshold for the classification. Results show that the proposed scheme can achieve centimeter level positioning accuracy.

Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance-level energy consumption from the aggregate consumption data of households. While there is significant interest from academia and industry, NILM techniques are still not adopted widely across households. This is mainly because the techniques developed for one household cannot be generalized and applied in other households (applicability), and require tremendous hand-tuning to apply across households (scalability). To overcome the above issues, we propose a novel semi-supervised energy disaggregation framework -- UniversalNILM. The key idea of UniversalNILM is to model appliances in a few training houses, which has detailed appliance-level data and transfer this learning on to test houses (blind disaggregation), which has only aggregate house consumption to derive fine-grained appliance energy consumption. UniversalNILM was empirically evaluated across datasets and outperforms the reported accuracy from both state-of-the-art supervised and unsupervised NILM techniques.

The smart grid vision has resulted in many demand side innovations such as nonintrusive load monitoring techniques, residential micro-grids, and demand response programs. Many of these techniques need a detailed residential network model for their research, evaluation, and validation. In response to such a need, this paper presents a sequential Monte Carlo (SMC) simulation platform for modeling and simulating low voltage residential networks. This platform targets the simulation of the quasi-steady-state network condition over an extended period such as 24 h. It consists of two main components. The first is a multiphase network model with power flow, harmonic, and motor starting study capabilities. The second is a load/generation behavior model that establishes the operating characteristics of various loads and generators based on time-of-use probability curves. These two components are combined together through an SMC simulation scheme. Four case studies are presented to demonstrate the applications of the proposed platform.