Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or auxiliary equipments, we make use of easily accessible knowledge of appliances and the sparsity of the switching events to design a Sparse Switching Event Recovering (SSER) method. By minimizing the total variation (TV) of the (sparse) event matrix, SSER can effectively recover the individual energy consumption values from the aggregated ones. To speed up the process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve the problem in active epochs of appliance activities in parallel. Using real-world trace data, we compare the performance of our method with that of the state-of-the-art solutions, including Least Square Estimation (LSE) and iterative Hidden Markov Model (HMM). The results show that our approach has an overall higher detection accuracy and a smaller overhead.
The performance of signature based approaches depends greatly on the uniqueness of an appliance's signature. In practice, however, the signatures of different appliances may overlap with each other, causing inaccurate event detection. Even for the same type of appliances, it may be hard to obtain the widely acceptable signature @cite_4 . In other words, it is hard to generalize the signature learned from a particular device's operating data. Consequently, even though a method may have a good performance over a specific group of appliances, it may suffer in other datasets, caused by the over-fitting problem due to over-specific signatures. Due to these difficulties, it is not easy to use signature based methods for unambiguous appliance detection and classification.
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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 . The data collected by smart meters contain a lot of useful information. One potential use of the data is to track the energy consumptions and operating statuses of major home appliances. The results will enable homeowners to make sound decisions on how to save energy and how to participate in demand response programs. This paper presents a new method to breakdown the total power demand measured by a smart meter to those used by individual appliances. A unique feature of the proposed method is that it utilizes diverse signatures associated with the entire operating window of an appliance for identification. As a result, appliances with complicated middle process can be tracked. A novel appliance registration device and scheme is also proposed to automate the creation of appliance signature database and to eliminate the need of massive training before identification. The software and system have been developed and deployed to real houses in order to verify the proposed method. We present an offline signature verification system using three different pseudo-dynamic features, two different classifier training approaches and two datasets. One of the most difficult problems of off-line signature verification is that the signature is just a static image while losing a lot of useful dynamic information. Three separate pseudo-dynamic features based on gray level: local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG) are used. The classification is performed using writer-dependent Support Vector Machine (SVMs) classifiers and Global Real Adaboost method, where two different approaches to train the classifier. In the first mode, each SVM is trained with the feature vectors obtained from the reference signatures of the corresponding user and those random forgeries for each signer while the global Adaboost classifier is trained using genuine and random forgery signatures of signers that are excluded from the test set. The fusion of all features achieves the best result of 7.66 and 9.94 equal error rate in GPDS while 7.55 and 11.55 equal error rate in CSD respectively. Home appliance networks now have the capability of integrating different features of independent appliances to provide value-added services. Concurrent execution of these services, however, can cause unexpected problems, even when each service is independently correct. This paper addresses the issue of detecting such interactions between services. We propose an approach that consists of two steps. In the first step, a model is developed to capture the behavior of the services and the interactions between them and users. In the second step, the model is automatically analyzed to see if possible interactions exist. This automatic analysis can be effectively performed with model checking techniques. The usefulness of the proposed approach is demonstrated through a case study, where several interactions were successfully detected.