Non-Intrusive Appliance Load Monitoring System Research Articles

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

Towards automatic setup of non intrusive appliance load monitoring – feature extraction and clustering

<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.</p>

ZigBee-Based Real-Time Energy Disaggregation System Using Factorial Hidden Markov Model

Energy consumption data of individual appliances at home can be obtained by nonintrusive appliance load monitoring (NILM) which aims at disaggregating total amount of power consumption into that of each appliance. NILM can be implemented utilizing pattern recognition algorithms, one of which is Factorial Hidden Markov Model (FHMM) that was employed in previous researches. In spite of the known importance of real-time disaggregation system, it has not been actively studied compared to the batch system. This study suggests a prototype of real-time NILM system via ZigBee smart plug and FHMM algorithm.

Development of a real-time non-intrusive appliance load monitoring system: An application level model

The research “Behavior Change and Energy Use” (US Department of Energy and Climate Change, 2011) [1] shows that with better information in the monthly electricity bill, the Energy Performance Certificate (EPC) can encourage people to reduce their energy usage. That is why smart meters - the emerging technology to help people to know their monthly energy consumption, are gradually replacing mechanical power meters. In this paper, we investigate a special energy monitoring process named Non-Intrusive Appliance Load Monitoring (NIALM), which is potentially the best method to give consumers pertinent information with respect to power consumption. However, real-time feedback feature in a low cost NIALM system is still a big challenge in such technology because of the complication in NIALM’s algorithms. System on Chip (SoC) technology can solve this challenge. Besides including high-speed interconnection and multi-processors, integrating Field-Programmable Gate Array (FPGA) into SoCs may be the most important evolution, which provides developers a powerful tool to develop a low cost but high performance system. Therefore, in this paper we proposed a development of a real-time NIALM system based on the SoC with FPGA acceleration.

Cost effective disaggregation mechanism for the NIALM system

Non-intrusive appliance load monitoring (NIALM) technology enables to detect the amount of power usage of each home appliance with single point sensing. In order to improve the performance (i.e., disaggregation/classification accuracy), features such as real power and harmonic components of electrical current of appliances are considered. However, taking more features into an account for better performance also increases the total cost of NIALM-related system. In this paper, a cost effective approach for NIALM is presented. With measured real power data sampled at low speed (about 1~2 Hz) from a single power line, classification of home appliances with high accuracy (about 80%) is achievable based on the proposed NIALM mechanism. The proposed mechanism is based on the real power time window pattern during a transient state and the real power level in a steady state after transition. The performance of the proposed mechanism is evaluated through simulation; classification accuracy is improved by 28% compared to the conventional NIALM approach based on real power delta scheme and 17% compared to the time window pattern.

Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation

Traditionally, electricity energy consumption of a residence can be monitored through the watt-hour meter installed at the panel. However, the power consumption of individual appliances cannot be identified directly. To track and control the energy consumption of each appliance, many meters are needed. This approach results in high financial costs. To reduce the installation costs, the Non-Intrusive Appliance Load Monitoring (NIALM) approach was proposed recently. In this paper, a novel Adaptive Non-Intrusive Appliance Load Monitoring (ANIALM) system that integrates appliance energizing and de-energizing transient feature extraction methods with soft-computing techniques is developed to keep track of the energy consumption of each appliance. The energizing and de-energizing transient responses of appliances can be captured through the analyses of ANIALM. Two recognizers, k-Nearest Neighbor Rule (k-NNR) and Back-Propagation Artificial Neural Network (BP-ANN), are used to identify different types of appliances and their operation status under different single-load and multiple-load operation scenarios. The Artificial Immune Algorithm (AIA) with the Fisher criterion is employed to adaptively adjust the feature parameters in order to improve the identification performance of recognizers when a new type of appliance is added for monitoring. From the experimental results obtained in different actual environments, the proposed ANIALM system is confirmed that it is able to identify the operation status of appliances. Also, although the generalization of both recognizers is similar and excellent, the k-NNR recognizer used by the ANIALM system is preferred from the aspects of recognizers’ identification mechanism and training performance due to its simplicity in computation and implementation.

Nonintrusive appliance load monitoring: Review and outlook

Consumer systems for home energy management can provide significant energy saving. Such systems may be based on nonintrusive appliance load monitoring (NIALM), in which individual appliance power consumption information is disaggregated from single-point measurements. The disaggregation methods constitute the most important part of NIALM systems. This paper reviews the methodology of consumer systems for NIALM in residential buildings.

Electric Load Disaggregation in Smart Metering Using a Novel Feature Extraction Method and Supervised Classification

Improving energy efficiency by monitoring household electrical consumption is of significant importance with the present-day climate change concerns. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched on appliances. This paper complements a novel feature extraction scheme presented in a previous work for load disaggregation with a comparative study of supervised classification methods. The objective of the current work is hence to make use of the feature extraction scheme to construct a database of signatures and then to compare different supervised learning methods for load classification. Preliminary results indicate high classification accuracy of all tested methods.

Nonintrusive monitoring of electric loads

The authors describe how the Electric Power Research Institute (EPRI) commissioned an implementation and commercialization of a nonintrusive appliance load monitoring system (NIALMS) based on EPRI-developed intellectual property. The system determines the energy consumption of individual appliances being turned on and off within a whole building's electric load.