Load Monitoring Solutions for the Smart Buildings – An Overview
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paper_title: Non-Intrusive Hybrid Energy Monitoring System

paper_content:

When the optimization of the manufacturing units is considered, the management usually focus on equipment costs and technology parameters. However, being the rising price of the energy, the energy efficiency has become a sensitive issue. Detailed energy related information should be collected in order to understand the consumption profile of each relevant machine/process and identify potential energy savings. Data gathering is time-consuming and very expensive since individual metering devices have to be used for each consumer. To overcome this inconvenient, several solution have been reported. All of them are making use of a single metering device (monitoring the overall energy consumption) and some Load Signature Identification algorithms, used to disaggregate the overall energy between the identified consumers. Their relatively low detection rate (about 80-90%) hindered the spread of LSI-based architectures. In this context, a new hybrid architecture is proposed in this paper, together with it's hybrid LSI algorithm. Several test performed in an electronic prototyping facility result in a detection rate close to 100%.
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paper_title: PowerPlay: creating virtual power meters through online load tracking

paper_content:

Online load tracking is the problem of monitoring an individual electrical load's energy usage by analyzing a building's smart meter data. The problem is important, since many energy optimizations require fine-grained, per-load energy data in real time; it also differs from the well-studied problem of load disaggregation in that it emphasizes efficient, online operation and per-load accuracy, rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to it. To enable high performance, we take a model-driven approach that focuses on efficiently detecting a small number of identifiable load features in smart meter data. Our results demonstrate that our system, called PowerPlay, i) enables efficient online tracking on low-power embedded platforms, ii) scales to thousands of loads (across many buildings) on server platforms, and iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm.
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paper_title: Hardware and software architecture of a smart meter based on electrical signature analysis

paper_content:

The main resources used in electricity generation are becoming fewer, while the impact of renewable resources is still quite small. To allow the necessary time for the technology of renewable resources to mature, a requirement is to increase the energy efficiency. In this regard the smart grid concept was implemented, but whose efficiency depends on creation of a framework through which the end user can actively participate in the energy marketplace. End users must be able to understand how the energy is used so that they could find possibilities to increase the efficiency of energy. Within this paper the architecture of a smart system (smart meter) capable to disaggregate the whole energy consumption into individual consumers is presented. This way, the user will have a better understanding on how the energy is used, will find major consumers and will be able to reduce his consumption.
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paper_title: Power quality assessment for microgrid scenarios

paper_content:

This paper deals with the rising issue of dealing with power quality issues, and aims at assessment of the impact of new smart technologies on existing electrical distribution systems from the reliability point of view. A main point in the analysis performed in this work is the characterization of the electric loads present in the upcoming micro-grids. The power quality assessment has then been performed by taking into account various generation and consumption characteristics, designed to cover the majority of cases. The paper addressed the impact of distributed generation on both distribution systems operation and energy management systems.
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paper_title: Feedback on household electricity consumption: a tool for saving energy?

paper_content:

Improved feedback on electricity consumption may provide a tool for customers to better control their consumption and ultimately save energy. This paper asks which kind of feedback is most successful. For this purpose, a psychological model is presented that illustrates how and why feedback works. Relevant features of feedback are identified that may determine its effectiveness: frequency, duration, content, breakdown, medium and way of presentation, comparisons, and combination with other instruments. The paper continues with an analysis of international experience in order to find empirical evidence for which kinds of feedback work best. In spite of considerable data restraints and research gaps, there is some indication that the most successful feedback combines the following features: it is given frequently and over a long time, provides an appliance-specific breakdown, is presented in a clear and appealing way, and uses computerized and interactive tools.
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paper_title: Estimation of variable-speed-drive power consumption from harmonic content

paper_content:

Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.
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paper_title: Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

paper_content:

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.
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paper_title: Modeling and Estimating Current Harmonics of Variable Electronic Loads

paper_content:

This paper develops a model for relating input current harmonic content to real power consumption for variable electronic loads, specifically for loads' actively controlled inverters energized by an uncontrolled rectification of the utility. This model serves as the basis for a method for estimating and disaggregating the power consumption of variable speed drives (VSDs) and rectifier loads from other constant power loads. This method can be used for nonintrusive power monitoring. The approach described in this paper uses the approximate switching function of the rectifier to derive the best estimating function for the fundamental current harmonic from a finite set of current harmonics uniquely associated with the operation of the drive. Experimental results show that the proposed VSD power and harmonic estimator can track VSD power consumption for monitoring given knowledge or an estimate of the input current harmonic content.
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paper_title: Residential Appliances Identification and Monitoring by a Nonintrusive Method

paper_content:

The method presented in this paper is based on new residential appliances classification and an events detect model. The whole identification and monitoring system is designed in a financially viable and easily applicable method that is ensured by followed two aspects: first, the only senor is one a meter installed to the main electric supply of a residence; second, the model of appliance is absolutely unsupervised. The classification factors of the appliances are main power consumption unit and working styles. The new events detector is simple enough to install into the sensor/meter. The final identification uses mean-shift clustering and multidimensional linear discriminates. Actual trials in situ and their results are presented to reveal the performance of the system.
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paper_title: Nonintrusive appliance load monitoring: Review and outlook

paper_content:

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.
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paper_title: Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

paper_content:

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.
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paper_title: Non-Intrusive Load Monitoring: A Review and Outlook

paper_content:

With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly. NILM estimates the power consumption of individual devices given their aggregate consumption. In this way, the combined consumption must only be monitored at a single, central point in the household, providing various advantages such as reduced cost for metering equipment. In this paper we discuss the fundamental building-blocks of NILM, first giving a taxonomy of appliance models and device signatures and then explaining common supervised and unsupervised learning methods. Furthermore, we outline a fundamental algorithm that tackles the task of NILM. Subsequently, this paper reviews recent research that has brought novel insight to the field and more effective techniques. Finally, we formulate future challenges in the domain of NILM and smart meters.
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paper_title: Nonintrusive appliance load monitoring based on an optical sensor

paper_content:

With the help of the optical sensor described in this paper, a NIALM system can be integrated with little expense in the existing electrical installation of households for analysing and optimising the active electric power consumption. The monitoring system requires as input device the electromechanical three-phase electricity meter (Ferraris meter) installed in the most of the German households. The active power consumption can be measured with time resolution down to second. The measured data constitute the basis for an autonomous NIALM approach which can without manual initialisation phase extract the switching sequence structures of the chief consumer load devices in the household, from the total load trace. The focus of the analysis lies on the automatic controlled on-off consumer loads. The analytical procedure consists of an approach based on certain rules for recognising repetitive patterns. In the decomposition of the total load as a function of time into switching events, simple on-off consumer loads and combinations of these patterns are investigated. The recognised patterns can be stored in a neural network. The network is updated daily and weekly. The results of the analysis are evaluated with regard to the potential for demand optimisation or for controlling the energy consumption of individual electric consumer devices.
---
paper_title: Residential Appliances Identification and Monitoring by a Nonintrusive Method

paper_content:

The method presented in this paper is based on new residential appliances classification and an events detect model. The whole identification and monitoring system is designed in a financially viable and easily applicable method that is ensured by followed two aspects: first, the only senor is one a meter installed to the main electric supply of a residence; second, the model of appliance is absolutely unsupervised. The classification factors of the appliances are main power consumption unit and working styles. The new events detector is simple enough to install into the sensor/meter. The final identification uses mean-shift clustering and multidimensional linear discriminates. Actual trials in situ and their results are presented to reveal the performance of the system.
---
paper_title: Nonintrusive appliance load monitoring: Review and outlook

paper_content:

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.
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paper_title: Nonintrusive appliance load monitoring

paper_content:

A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described. The theory and current practice of nonintrusive appliance load monitoring are discussed, including goals, applications, load models, appliance signatures, algorithms, prototypes field-test results, current research directions, and the advantages and disadvantages of this approach relative to intrusive monitoring. >
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paper_title: A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof

paper_content:

A load signature is an electrical expression that a load device or appliance distinctly possesses. Load signatures can be applied to produce many useful services and products, such as, determining the energy usage of individual appliances, monitoring the health of critical equipment, monitoring power quality, and developing facility management tools. Load signatures of typical yet extensive loads are needed to be collected before applying them to different services and products. As there are an enormous number of electrical appliances, it is beneficial to classify the appliances for building a well-organized load signature database. The objective of this study is to develop an effective method to classify the loads. A 2-dimensional form of load signatures, voltage-current (V-l) trajectory, is suggested for characterizing the typical household appliances.Hierarchical clustering method was employed to classify the appliances and construct the taxonomy of the appliances. The taxonomy based on V-l trajectory was compared to the taxonomies based on traditional power metrics and eigenvectors in the previous studies. It was found that the groups of appliances in the taxonomy based on V-I trajectory were well-separated and had engineering meanings .
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paper_title: Using appliance signatures for monitoring residential loads at meter panel level

paper_content:

The author presents an initial approach to identifying electrical household appliances strictly by analyzing variations in current-voltage signals at the meter panel level. The study shows that local measurement of variations in active and reactive power is sufficient in most cases to identify an appliance. This measurement must be synchronized with the events. This necessitates a numerical measurement device which can monitor the signal and isolate the times when variations occur. In a second stage, a sorting algorithm identifies the appliance that has operated. An attempt is made to show the feasibility of this device. Test findings secured on site using a test prototype are presented. >
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paper_title: Appliance Recognition from Electric Current Signals for Information-Energy Integrated Network in Home Environments

paper_content:

We are developing a novel home network system based upon the integration of information and energy. The system aims to analyze user behavior with a power-sensing network and provide various life-support services to manage power and electric appliances according to user behavior and preferences. This paper describes an electric appliance recognition method using power-sensing data measured by CECU (Communication and Energy Care Unit) which is an intelligent outlet with voltage and current sensors to integrate legacy appliances (which are incompatible with a communications network) with the home network. Furthermore, we demonstrate a prototype home energy management system and examples of services based upon appliance recognition.
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paper_title: Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring

paper_content:

Nonintrusive load monitoring (NILM) is a technique for deducing the power consumption and operational schedule of individual loads in a building from measurements of the overall voltage and current feeding it, using information and communication technologies. In this article, we review the potential of this technology to enhance residential electricity audits. First, we review the currently commercially available whole-house and plug-level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system installed in an apartment unit. Recommendations for improving the technology to allow detailed, continuous appliance-level auditing of residential buildings are provided, along with ideas for possible future work in the field.
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paper_title: PowerPlay: creating virtual power meters through online load tracking

paper_content:

Online load tracking is the problem of monitoring an individual electrical load's energy usage by analyzing a building's smart meter data. The problem is important, since many energy optimizations require fine-grained, per-load energy data in real time; it also differs from the well-studied problem of load disaggregation in that it emphasizes efficient, online operation and per-load accuracy, rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to it. To enable high performance, we take a model-driven approach that focuses on efficiently detecting a small number of identifiable load features in smart meter data. Our results demonstrate that our system, called PowerPlay, i) enables efficient online tracking on low-power embedded platforms, ii) scales to thousands of loads (across many buildings) on server platforms, and iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm.
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paper_title: ANNOT: Automated Electricity Data Annotation Using Wireless Sensor Networks

paper_content:

Recent advances in low-power wireless networking have enabled remote and nonintrusive access to households' electric meter readings, allowing direct real-time feedback on electricity consumption to home owners and energy providers. Fine-grained electricity billing based on appliance power load monitoring has been investigated for more than two decades, but has not yet witnessed wide commercial acceptance. In this paper, we argue that the required human supervision for profiling and calibrating appliance load monitoring systems is a key reason preventing large-scale commercial roll-outs. We propose ANNOT, a system to automate electricity data annotation leveraging cheap wireless sensor nodes. Characteristic sensory stimuli captured by sensor nodes placed next to appliances are translated into appliance operating state and correlated to the electricity data, autonomously generating the annotation of electricity data with appliance activity. The system is able to facilitate the acquisition of appliance signatures, training data and validate the monitoring output. We validate the concept by integrating the automated annotation system to the RECAP appliance load monitoring system.
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paper_title: Detecting patterns of appliances from total load data using a dynamic programming approach

paper_content:

Nonintrusive appliance load monitoring (NIALM) systems require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem but the loss of information of the measured data has to be compensated by more intelligent algorithms and implemented rules to disaggregate the total load trace of only the active power measurements. The paper presents a NIALM approach to analyse data, collected from a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of clustering methods, a genetic algorithm and a dynamic programming approach is presented. The genetic algorithm is used to combine frequently occurring events to create hypothetical finite state machines to model detectable appliances. The time series of each finite state machine is optimized using a dynamic programming method similar to the viterbi algorithm.
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paper_title: Power signature analysis

paper_content:

Nonintrusive load monitoring (NILM) can determine operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. NILM is an ideal platform for extracting useful information about any system that uses electromechanical devices. It has a low installation cost and high reliability because it uses a bare minimum of sensors. It is possible to use modem state and parameter estimation algorithms to verify remotely the "health" of electromechanical loads by using NILM to analyze measured waveforms associated with the operation of individual loads. NILM can also monitor the operation of the electrical distribution system itself, identifying situations where two or more otherwise healthy loads interfere with each other's operation through voltage waveform distortion or power quality problems. Strategies for nonintrusive monitoring have developed over the last 20 years. Advances in computing technology make a new wealth of computational tools useful in practical, field-based NILM systems. This article reviews techniques for high-performance nonintrusive load and diagnostic monitoring and illustrates key points with results from field tests.
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paper_title: Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses

paper_content:

Abstract The method presented in this paper shows a promising potential for application in residential buildings. The results prove that the whole-house electricity consumption can be disaggregated into its major end-uses, using a pattern recognition approach and only one sensor installed on the main electric entrance of the house. It also required a one-time submetering of the target appliances during the training period, of about a week, to find the electric characteristics of appliances. The results are provided in terms of daily load profiles, energy consumption and energy contribution of selected appliances. The proposed method was tested with monitored data from 3 weeks: (i) the training period of 1 week in October, (ii) the near-to-date testing period of 1 week in November and (iii) the far-to-date testing period of 1 week in January. For instance, the difference between monitored and estimated contribution is, for the month of October 1996, as follows: (i) 13 kW h or $0.85 for the DHW heater and (ii) 6 kW h or $0.36 for the refrigerator. The overall difference for both appliances does not exceed $1.25 for the month of October, for a total electricity bill of 912 kW h and $60.60, which appears to be acceptable for every homeowner. The errors in evaluating the daily energy consumption is between −10.5% and 15.9% for both the DWH heater and the refrigerator.
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paper_title: Nonintrusive appliance load monitoring based on an optical sensor

paper_content:

With the help of the optical sensor described in this paper, a NIALM system can be integrated with little expense in the existing electrical installation of households for analysing and optimising the active electric power consumption. The monitoring system requires as input device the electromechanical three-phase electricity meter (Ferraris meter) installed in the most of the German households. The active power consumption can be measured with time resolution down to second. The measured data constitute the basis for an autonomous NIALM approach which can without manual initialisation phase extract the switching sequence structures of the chief consumer load devices in the household, from the total load trace. The focus of the analysis lies on the automatic controlled on-off consumer loads. The analytical procedure consists of an approach based on certain rules for recognising repetitive patterns. In the decomposition of the total load as a function of time into switching events, simple on-off consumer loads and combinations of these patterns are investigated. The recognised patterns can be stored in a neural network. The network is updated daily and weekly. The results of the analysis are evaluated with regard to the potential for demand optimisation or for controlling the energy consumption of individual electric consumer devices.
---
paper_title: Using appliance signatures for monitoring residential loads at meter panel level

paper_content:

The author presents an initial approach to identifying electrical household appliances strictly by analyzing variations in current-voltage signals at the meter panel level. The study shows that local measurement of variations in active and reactive power is sufficient in most cases to identify an appliance. This measurement must be synchronized with the events. This necessitates a numerical measurement device which can monitor the signal and isolate the times when variations occur. In a second stage, a sorting algorithm identifies the appliance that has operated. An attempt is made to show the feasibility of this device. Test findings secured on site using a test prototype are presented. >
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paper_title: Indices for assessing harmonic distortion from power quality measurements: definitions and benchmark data

paper_content:

For many years, electricity distribution companies have used sustained interruption indices as indicators of the reliability of service provided on their systems. Today, however, many electricity consumers are adversely affected by more subtle voltage disturbances such as harmonic distortion. In response to the increased sensitivity of end-use equipment, many utilities are implementing extensive monitoring systems to assess service quality levels. Such monitoring systems yield massive databases of service quality data. This paper presents indices developed to quantify system service quality with respect to measured harmonic distortion levels. Example benchmark values for the indices are calculated using data from a national distribution power quality data collection project.
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paper_title: Concurrent simulation platform for energy-aware smart metering systems

paper_content:

We propose a simulation framework that can model a house equipped with various home appliances and next-generation smart metering devices. This simulator can predict the power dissipation profiles of individual appliances as well as the cumulative energy consumption of the house in a realistic manner. We utilize SystemC, a concurrent system-modeling methodology originally developed and populated in the design automation community. According to our experiments with various consumer electronics devices, the simulated and measured power profiles match very closely, producing the average correlation of 0.973. The deviation of simulated energy consumption from the measurement was also negligible. Using the proposed simulation platform, any electricity consumer interested in energy saving as well as the designer of a new smart metering system will be able to simulate and test their system from energy perspectives. As a case study, we show how the size of the accumulative power peak of a house can be reduced significantly by using the information provided by the proposed simulator.
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paper_title: Look Back before Leaping Forward: Four Decades of Domestic Energy Inquiry

paper_content:

Reducing domestic energy consumption has a long, varied history, embracing pervasive computing, technology, sociology, and economics. The authors provide an overview and key references to help readers explore this rich field's background more deeply and broadly.
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paper_title: Nonintrusive appliance load monitoring

paper_content:

A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described. The theory and current practice of nonintrusive appliance load monitoring are discussed, including goals, applications, load models, appliance signatures, algorithms, prototypes field-test results, current research directions, and the advantages and disadvantages of this approach relative to intrusive monitoring. >
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paper_title: Training load monitoring algorithms on highly sub-metered home electricity consumption data

paper_content:

Abstract The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the development of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.
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paper_title: State of art on load monitoring methods

paper_content:

This paper deals with a state of art about the most non-intrusive load monitoring (NILM) methods. These methods allow identifying consumption of the electrical loads. In the context of sustainable development, this is of growing interest and rapid evolution subject in recent years. The objective is to manage energy consumption by the determination of the operating schedule of individual appliances. These approaches have been developed to simplify the collection of energy consumption data. This technique for gathering appliance load information doesnpsilat require placing sensors on individual appliances, and hence an intrusion into the energy consumerpsilas property. This paper proposes an overview of the most popular methods and describes their advantages and disadvantages. It opens new perspectives which are under development in our laboratory. Our goal is to reduce the complexity of monitoring appliances and to find the most efficient method for identification.
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paper_title: ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

paper_content:

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.
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paper_title: Energy Disaggregation via Discriminative Sparse Coding

paper_content:

Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having device-level energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in the effort to maximize energy conservation. In this paper, we examine a large scale energy disaggregation task, and apply a novel extension of sparse coding to this problem. In particular, we develop a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance. We show that this significantly improves the performance of sparse coding algorithms on the energy task and illustrate how these disaggregation results can provide useful information about energy usage.
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paper_title: Neural-network-based signature recognition for harmonic source identification

paper_content:

This paper proposes a neural-network (NN)-based approach to nonintrusive harmonic source identification. In this approach, NNs are trained to extract important features from the input current waveform to uniquely identify various types of devices using their distinct harmonic "signatures". Such automated, noninvasive device identification will be critical in future power-quality monitoring and enhancement systems. Several NN-based classification models including multilayer perceptron (MLP), radial basis function (RBF) network, and support vector machines (SVM) with linear, polynomial, and RBF kernels were developed for signature extraction and device identification. These models were trained and tested using spike train data gathered from the Fourier analysis of the input current waveform in the presence of multiple devices. The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed that MLPs and SVM were both able to determine the presence of devices based on their harmonic signatures with high accuracy. MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.
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paper_title: Nonintrusive appliance load monitoring

paper_content:

A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described. The theory and current practice of nonintrusive appliance load monitoring are discussed, including goals, applications, load models, appliance signatures, algorithms, prototypes field-test results, current research directions, and the advantages and disadvantages of this approach relative to intrusive monitoring. >
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Title: Load Monitoring Solutions for the Smart Buildings – An Overview

Section 1: INTRODUCTION
Description 1: In this section, introduce the importance of load monitoring in smart buildings and describe the basic concepts of Intrusive Load Method (ILM) and Non-Intrusive Load Method (NILM).

Section 2: System structure
Description 2: Describe the general framework of NILM, including data acquisition and data processing, with a focus on the hardware components and factors affecting NILM performance.

Section 3: Appliance types
Description 3: Explain different categories of electrical appliances, such as resistive, inductive, and capacitive loads, and their energy consumption patterns. Discuss permanent appliances, on/off devices, and appliances with multiple states of operation.

Section 4: THE ISSUE OF THE ELECTRICAL APPLIANCE SIGNATURE
Description 4: Detail the concept of electrical appliance signatures, including steady-state and transient modes. Discuss the parameters used to describe these signatures and the challenges in identifying them.

Section 5: Load signature in steady state mode
Description 5: Discuss steady-state analysis of power signals and how it is used to detect electrical consumers. Mention key techniques and methods used in this analysis along with their limitations.

Section 6: Optimal Sampling Frequency for Signature Detection
Description 6: Explain the importance of sampling frequency in detecting appliance signatures, comparing high-frequency and low-frequency sampling rates and their impact on NILM effectiveness.

Section 7: CONCLUSIONS
Description 7: Summarize the current solutions for power consumption monitoring, comparing intrusive and non-intrusive methods, and evaluate their effectiveness and economic aspects.