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.
@cite_6: Source separation of whole-home electrical consumption also known as energy disaggregation plays a crucial role in energy savings and sustainable development. One important approach towards accurate energy disaggregation is based on sparse code learning. The sparsity-based source separation algorithms allow to build models that explicitly generalize across multiple different devices of the same category. While this method has recently been investigated, yet the importance of the degree of sparseness given by the regularization parameter is rarely considered. In this paper we aim at investigating the performance of learning representations from the aggregated electrical load signal with sparse models for energy disaggregation. In particular we focus our study on the influence of the regularization parameter in the overall approach. The computational experiments yielded in real data from home electrical energy consumption show that for several degrees of sparseness a reliable scheme for energy disaggregation can be obtained with statistical significance.
@cite_24: 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.
@cite_2: To smartly control the massive electrical appliances in buildings to save energy, the real-time on off states of the electrical appliances are critically required as the fundamental information. However, it is generally a very difficult and costly problem, because N appliances have 2N states and the appliances are massively in modern buildings. This paper propose a novel compressive sensing model for monitoring the massive appliances' states, in which the sparseness of on off switching events within a short observation interval is exploited. Based on such a temporal sparseness feature, a lightweight state tracking framework is proposed to track the on off states of N appliances by deploying only m smart meters on the power load tree, where m L N. Particularly, it firstly presents an online state decoding algorithm based on a Hidden Markov Model of sparse state transitions. It reduces the traditional O(t22N) complexity of Viterbi decoding to polynomial complexity of O(tnU+1) where n
@cite_16: Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.
@cite_20: Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.
A number of methods made use of state transition in appliances' activities for energy disaggregation. Recently, the Hidden Markov Model (HMM) was adopted to model the state transition patterns of appliances. The hidden states of each appliance at each time instant are predicted by inference algorithms, such as the Viterbi algorithm, with the observed emission probabilities @cite_16 @cite_20 . Non-negative sparse coding was proposed to solve the energy disaggregation problem in @cite_24 . It was further discussed in @cite_6 , in which a training process was needed to obtain the basis vector related to the state transition patterns of different appliances. Although some other works, such as @cite_2 , were not to solve the energy disaggregation problem, they also utilized the appliance state transition information and their results may be helpful for energy disaggregation.