Independent-Variation Matrix Factorization With Application to Energy Disaggregation

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

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

Overview of non-intrusive load monitoring and identification techniques

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

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

This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix factorization and sparse approximation, a novel approach is presented that differs from classical clustering methods, such as seminonnegative matrix factorization, K -EVD, or k -means clustering, yet combines some aspects of all these. As in nonnegative matrix factorization and K -EVD, the matrix decomposition is iteratively refined to optimize a data fidelity term; however, no positivity constraint is enforced directly nor do we need to explicitly compute eigenvectors. As in k -means and K -EVD, each optimization step is followed by a hard cluster assignment. This leads to an efficient algorithm that is shown here to outperform common competitors in terms of clustering performance and/or computation speed. In addition to a detailed theoretical analysis of some of the algorithm's main properties, the approach is empirically evaluated on a range of toy problems, several standard text clustering data sets, and a high-dimensional problem in brain imaging, where functional magnetic resonance imaging data are used to partition the human cerebral cortex into distinct functional regions.

A requirement of Smart Grids is the ability to predict the energy consumption patterns of their users. In the residential domain, this is usually not feasible due to the inability of the grid to dialog with (legacy) domestic appliances. To overcome this issue Non Intrusive Load Monitoring (NILM) was introduced, a task in which a predictor is used to disaggregate household power consumption. Many of the newer approaches make use of Neural Networks to accomplish this task, due to their superior ability to detect patterns in temporal (thus sequential) data. These models unfortunately require a huge amount of data to achieve good performance, and have the tendency to overfit the training data, making them difficult to predict future consumptions. For these reasons, adapting them to optimally predict a (future) house’s consumption requires expensive and often prohibitive data collection phases. We propose a solution in the form of a neuro-symbolic framework that refines neural network predictions via a constrained optimization problem modelling the characteristics of the appliances of a house. This combined approach achieves superior performance with respect to the neural network alone over two out of five appliances and comparable results for the remaining ones, without requiring further training data.

VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns

Due to the increase in electricity demand and the rapid depletion of fossil fuels, energy management has become a critical issue over the last two decades. Thus, researchers, utility suppliers, governments, and policymakers are working in tandem to develop novel solutions. In recent years, solutions based on Intrusive Load Monitoring (ILM) and Non-intrusive Load Monitoring (NILM) have garnered the interest of many researchers. However, NILM systems are less difficult to implement and more cost-effective than ILM systems. Even though available NILM-based solutions can identify single-state devices with acceptable accuracy, identifying the various operating states of multi-state devices remains a problem. This research work proposes a novel supervised learning algorithm to correctly identify the operating states of multi-state residential devices. Results obtained through extensive simulations indicate that the proposed algorithm can achieve device and state identification accuracy of 93 percent and 91 percent, respectively.

Dynamic patterns of urban space reveals the interaction of human activities and urban space, and draws much attention from urban planners and decision makers. The most common way to extract dynamic patterns from city-wide level human dynamics is through Singular Value Decomposition (SVD). However, SVD constrains the columns of matrix factors to be orthonormal. Some of the entries of matrix factors must be negative which leads to difficulties in interpretation of the decomposed components when each component is physically required to be nonnegative, e.g. population density. Therefore, we propose using Semi-nonnegative Matrix Factorization (Semi-NMF) framework to extract dynamic patterns of city-wide level population density. Semi-NMF constrains dynamic patterns of population density to be nonnegative which results in parts-based representation and interpretable patterns. We apply our approach to a mobile phone dataset of Nanjing and extract interpretable dynamic patterns. Based on these patterns, we present application in urban planning to investigate commuter towns in Nanjing.

A non-intrusive load monitoring algorithm based on real-time feature extraction and deep learning model

A mixed-integer programming approach for industrial non-intrusive load monitoring

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home, and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms. The source code and our collected data (HVORUT) for studying NILM for HVAC units can be found at https://bitbucket.org/aicip/nonintrusive-load-monitoring.

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Supplementary information: Supplementary data are available at Bioinformatics online.Contact: elmar.lang@biologie.uni-regensburg.de

Semi Non-negative Matrix Factorization (SNMF) is a machine learning algorithm that is used to decompose large data matrices where the data matrix is unconstrained (i.e., it may have mixed signs). We develop the quantum version of SNMF using quantum gradient descent, and we show that the quantum version of SNMF provides an exponential speedup compared to its classical version. Our approach is well adapted for high-dimensional problems since in the context of using a quantum approach only a few number of iterations is enough to obtain a close approximation of the factorization. From the algorithmic analysis point of view, the proposed algorithm for Quantum Semi Non-negative Matrix Factorization (QSNMF) yields a performance \(T\mathcal {O}( polylog K (M+N)),\) while the classical version of Semi Non-negative Matrix Factorization (SNMF) yields a performance \(T\mathcal {O}(poly (K M N))\) where M and N are the dimensions of the matrix, K is the number of clusters and T is the maximum number of iterations to prepare the quantum states.