Individual Electrical Appliances Research Articles

Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI.

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.

2D Transformations of Energy Signals for Energy Disaggregation.

The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.

Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning

Non-Intrusive Load Monitoring (NILM) can be used to detect, recognize, and classify switching events of individual electrical appliances from an aggregate power signal that is measured at the main line of the grid connection. A limitation of existing solutions is that deep learning models tend to overfit the data and do not express their uncertainty when making predictions. This paper shows that uncertainty information can be obtained in a natural way by making use of Bayesian Neural Networks. Having this information is very valuable, because it supplies relevant information about potential misclassifications of the model to an end-user. The source of these misclassifications can be attributed to ambiguous data, or the model requiring more examples to learn from. In this work, an increase in generalization performance is observed when making use of Stochastic Gradient Hamiltonian Monte Carlo over Stochastic Gradient descent, and the usefulness of uncertainty in a NILM context is discussed.

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by a single smart-meter. In this article we introduce Double Fourier Integral Analysis in the Non-Intrusive Load Monitoring task in order to provide more distinct feature descriptions compared to current or voltage spectrograms. Specifically, the high-frequency aggregated current and voltage signals are transformed into two-dimensional unit cells as calculated by Double Fourier Integral Analysis and used as input to a Convolutional Neural Network for regression. The performance of the proposed methodology was evaluated in the publicly available U.K.-DALE dataset. The proposed approach improves the estimation accuracy by 7.2% when compared to the baseline energy disaggregation setup using current and voltage spectrograms.

A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting Facilitated by Energy Decomposition for Smart Home Automation

The key advantage of using power-utility-owned smart meters is the ability to transmit electrical energy consumption data to power utilities’ remote data centers for various purposes, such as billing. Several useful consumer-centric use cases can also be identified for the collection and further analysis of consumers’ electrical energy consumption data from smart meters. One of the use cases is home automation. Recent related solutions for home automation involving home security and healthcare depend on the installation of sensors and/or other devices such as video cameras, which have high costs for installation and annual maintenance. Because the electrical energy consumption patterns mined from smart meter data are indicative of residents’ daily life, it is possible to develop a new home automation approach based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy decomposition</i> for smart home automation. Accordingly, in this work, a smart home energy management system (SHEMS) utilizing a parallel-processing-implemented, GPU-accelerated neurocomputing-based time-series load modeling and forecasting mechanism is proposed for smart home automation. Energy decomposition is used to facilitate the time-series load modeling and forecasting mechanism, which tracks appliance-level electrical energy consumption to be quantitatively modeled from circuit-level consumption, with no intrusive deployment of networked plug-level power meters for individual electrical home appliances. For the neurocomputing approach applied in this mechanism, an autoregressive multilayer perceptron methodology is compared against a stacked long short-term memory methodology. The presented neurocomputing-based time-series load modeling and forecasting mechanism facilitated by energy decomposition is capable of predicting residents’ daily behavioral patterns by nonintrusively analyzing and modeling relevant electrical home appliances based on their past trends for smart home automation.

Electricity Consumption in Construction Projects in the Czech Republic

The paper deals with the development of electricity production in the Czech Republic and its consumption in the construction industry. The research specifies individual types of appliances - electric motors, lighting, heating, water heating and information technology in a selected newly constructed building. Construction machines with electric motors have been taken out of the itemized budget of the construction, their electric power input and time of their use have been determined. In order to determine other consumption, the equipment of a construction site, individual electrical appliances, their power input, number and time of their use have been proposed. Methods of consumption measuring and its division into the implementation costs of constructions in the way usual in the Czech Republic are presented. Furthermore the possibilities of optimizing electricity consumption costs within the project management by choosing a supplier and discussing other possible measures during the construction process have been considered.

Realistic scenario modelling for building power supply and distribution system based on non-intrusive load monitoring

In power supply and distribution system in buildings, the conventional designs of loads are fictitious, and it is difficult to find the vulnerabilities timely. In order to solve this problem, a realistic scenario modelling method is proposed. Aiming at the input of the power consumption data in realistic scenario model, a non-intrusive load monitoring method is used, combined with sliding window switching event detection method of electrical appliance, a sequence to short-sequence deep learning model is also established whose input vectors are composed of switching time and total power data. The input vectors in the deep learning model can be decomposed to the individual electrical appliance. Compared with CO and FHMM algorithm, the decomposition results of this model are more excellent in precision, recall, F1 score and accuracy. And it is more practical and accurate to replace the estimated data with the electricity consumption data obtained by NILM in the realistic scenario modelling.

Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects

Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.

Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects

Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

Simple analysis of energy consumption patterns recorded by smart meters can be used to deduce household occupancy. With access to higher-resolution smart-meter readings, we can infer more detailed information about the household including the use of individual electric appliances through non-intrusive load monitoring techniques. The extent of privacy concerns caused by smart meters has proved to an obstacle in the roll-out of smart meters in some countries. This highlights the need for investigating smart-meter privacy. Mechanisms for ensuring smart-meter privacy fall in broad categories of data manipulation, demand shaping, and load scheduling. In smart-meter data manipulation, the smart meter collects real, potentially high-resolution data about the energy consumption within the house. This data is then manipulated before communication with to utility providers and retailers. The manipulation could be non-stochastic, such as aggregation, binning, and down-sampling, or stochastic, such as additive noise. In demand shaping and load scheduling, smart-meter readings are communicated without any interference but the consumption is manipulated by renewable energy sources, batteries, or shifting loads to render non-intrusive load monitoring ineffective. In this study, the author reviews these approaches and presents several methods relying on homomorphic encryption, differential privacy, information theory, and statistics for ensuring privacy.

Review Paper on “Intelligent Energy Management”

t Energy management is now a days is subjected to a great importance and complexity. Today there are lots of energy measuring devices in the market and also for monitoring. These power consuming devices can be of any sector that is household ,industrial, commercial. Many a times, consumers aren’t satisfied with the bill as it does not show the power consumed at the device level. So with the new computer aided technologies where devices can be connected wirelessly leading to IoT, in this paper an IoT based Intelligent Energy Management system is introduced where appliances like Fan ,Bulb and other appliances can be monitored and controlled. This proposed system presents the design and implementation of an monitoring and controlling system using, Arduino nano microcontroller, IoT which can be used to measure and monitor the power consumed by any individual electrical appliance. The important motive of the proposed system is to monitor and control the power consumption as per the appliances.. The energy monitoring system calculates the power consumed by various electrical devices and displays it through a web server

A Scenario-Based Stochastic Optimization Approach for Non-Intrusive Appliance Load Monitoring

In this paper an energy disaggregation problem, commonly studied as “non-intrusive load monitoring”, is presented. Non-intrusive load monitoring is a procedure for estimating the energy profiles of individual electric home appliances from the aggregated power measurement. We present this problem as an optimization-based problem in which the least-square errors are minimized to find the set of active appliances, using the instantaneous aggregated power. The convex regularization terms are also added, which exploit the information of the probability of individual appliance usage and assume the behavior of the appliances’ power profiles to be constant as piecewise signal over time. The problem is formulated using a scenario-based stochastic optimization approach to consider the underlying uncertainties in power measurements. The expected value of the problem’s objective function is computed by using the sample average approximation method, where normally distributed samples of a random variable are introduced into the problem using the Monte Carlo simulation. Moreover, the appliances’ limits on the power modes are formulated as a chance constraint in the optimization problem. The training and testing of the proposed algorithm are done by using the benchmark data: AMPds and REFIT. The simulation results show that the proposed scenario-based stochastic optimization approach successfully estimates the energy profiles of individual appliances with multiple power modes.

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications – device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

Design of a Stand-Alone Rooftop PV System for Electrification of an Academic Building

Solar energy is one of the most promising options of renewable energy in the context of energy sustainability. Nowadays, as the utilization of solar energy has been continuously expanded in wide scale, researches related to the topic have been carried out all over the world. The prime focus of this study is to provide sustainable energy generation for an academic building located in a rural place, where power outage is a frequent issue. In this study, individual power system components have been suitably designed which could electrify the building for yearlong use. A rooftop photovoltaic (PV) system with three days battery backup has been considered for the present case. Designing of the PV system is based on the selection of individual electrical appliances and its operating time in a day. For this purpose, a survey has been carried out over a year in order to identify the day in which maximum power was utilized. The study revealed that the total estimated capacity of the stand-alone PV system should be 138.6 KWp in which 446 PV modules bearing 300 Wp each are connected together in series parallel combination. Total 656 numbers of batteries (12V- 200Ah each) are required for power backup which store the excess PV generation. Suitable size also been considered for inverters and charger controller which are connected in parallel and series respectively. The area required to install PV modules on the rooftop without shadow effect has been properly assessed. Besides being PV system design, brief cost analysis has been carried out in terms of simple payback period, unit cost of power generation and cash flow in terms of present value.

Non-Intrusive Load Monitoring Using Current Shapelets

Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.

Design and Implementation of Home Appliance Energy Monitoring Device

This paper presents the implementation of an energy monitoring device using Arduino Nano microcontroller to measure the power consumed by any individual electrical appliance in residential homes. It is also designed with the capability of sending measured electrical power parameters through SMS to owners when a request is made. The electrical energy and power are calculated based on measurements taken by Allegro ACS712 Hall effect current sensor and measured voltage over a time period. A novel feature of this device is its ability to also determine the start-up current of inductive loads. The device come in handy for homeowners who want to monitor the energy consumption of some appliances to reduce associated electricity bills. In addition, it could also be used for an energy audit of residential homes.

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “k-value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

Online end-use energy disaggregation via jump linear models

This paper presents two iterative algorithms for non-intrusive appliance load monitoring, which aims to decompose the aggregate power consumption only measured at the household level into the contributions of the individual electric appliances. The approaches are based on modelling the total power consumption as a combination of jump linear sub-models, each of them describing the behaviour of the individual appliance. Dynamic-programming and multi-model Kalman filtering techniques are used to reconstruct the power consumptions at the single-appliance level from the aggregate power in an iterative way.

Phase noise as power characteristic of individual appliance for non‐intrusive load monitoring

Phase noise, a unique characteristic of electrical appliances, for non-intrusive load monitoring (NILM) is proposed. NILM is a technique, which can distinguish the power consumption of an individual electrical appliance, without requiring the installation of sensors on the appliance, reducing the number of sensors required in an energy management system. It utilises the previously-recorded power supply characteristics of individual appliances and measures the changes in the bus voltage or current, which provide power supply to several appliances, thereby distinguishing the operational appliances without direct measurement. Considering electrical appliances that can be commonly found in a laboratory, such as an electric drill, soldering iron, and light bulb, the authors found that, when each was turned on, the current at an offset of 5 Hz from the centre frequency of 60 Hz was 61.4, 18.8, and 6.2 mA for each appliance, respectively. As each appliance was turned on in the sequence, the bus current providing electrical energy to all three appliances was measured to be 60.4, 11.1, and 5.7 mA, respectively. Thus, it was experimentally confirmed that the current at frequencies near the centre frequency, i.e. the phase noise, can be used for NILM.