# Context-Based Energy Disaggregation in Smart Homes
## 3 Related Work
### Disaggregation Approaches  
The NILM methods implemented so far can also be distinguished for the approach type. There are two ways to conceive the training phase of a learning method: supervised and unsupervised. Both of them have weaknesses and strengths [28]. A supervised approach makes use of labeled data in the training phase in order to allow the NILM system to detect device contributions from the aggregate consumption load [7]. Consequently, an increase in terms of both computational resource investments and human effort for the system startup phase has to be considered; however, it generally offers good accuracy results. Starting from [PERSON]'s work, in 1992 [6], which made use of Finite State Machine (FSM), many other different supervised approaches have been proposed, as those based on k-Nearest Neighbor (k-NN) [29] and Support Vector Machine (SVM) [26; 30]. [PERSON] _et al._[31] have recently performed an analysis for comparing disaggregation accuracy results achieved by different classifiers such as SVM, NN and Random Forests. As it has been shown that the temporal transitions information could improve the disaggregation [12], few algorithms that could manage this combination have been investigated. For instance, Artificial Neural Networks (ANN) have been used in many works as they offer better extensibility, dynamicity and capability to incorporate device state transition information such as in [13; 19; 25]. [PERSON] _et al._[13] proposed a supervised NILM system, called REcognition of electrical Appliances and Profiling (RECAP), based on a single ZigBee sensor for energy monitoring clipped to the main electrical unit. In an unsupervised approach, the system does not have any a priori knowledge about the devices and often requires a manual appliance labeling when the disaggregation phase has finished. In [32] the genetic k-means clustering has been used to isolate the Real Power and Reactive Power steady-states and to detect the number of the turned-ON devices. [PERSON] _et al._[33] propose an appliance behavior modeling approach which uses Hidden Markov Models on Real Power traces. One of the most recent and original unsupervised approaches is the one proposed by [PERSON] and [PERSON] [9] in 2012. This method consists in fact in modeling each appliance consumption behavior with a Hidden Markov Model and the aggregate consumption with the additive factorial version; the authors also proposed a new inference algorithm, called Additive Factorial Approximate MAP (AFAMAP) to separate appliances traces from the aggregated load data. [PERSON] _et al._[34] propose an approach based on additive FHMM that introduces the use of Particle Filtering for estimating the appliance states. Few recent projects have remarked on the need to provide the system with context information in order to both better characterize the appliance profiles and improve disaggregation performances. In 2011, [PERSON] _et al._[35] extended the FHMM approach with an unsupervised disaggregation algorithm that uses appliances behavior information (_i.e._, ON-duration, OFF-duration, dependency between appliances, _etc._). With respect to [PERSON]'s work, our original contribution is based on the addition of environmental and statistical features such as respectively the user presence and the daily usage distribution of several appliances. In addition, [PERSON] _et al._[36] proposed a similar approach which uses temporal and sensing information but with the aim of performing an appliance classification of power traces of single or a combination of two devices. Furthermore, a private dataset has been used in both [35] and [36], thus non-comparable results have been produced; conversely, our work uses a public dataset [10], which is thus available also to other researchers.