# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 1 Introduction
### Related Work  
The field of NILM and energy disaggregation is very extensive and spans different scientific areas. To understand various aspects of NILM (from electrical characteristics of appliances to data requirements and algorithms for appliance identification), some good points to start from are [7; 11; 12; 13; 14; 15; 16; 17; 18]. A small note on the sampling rate of the publicly available datasets is in order. A very recent review [19] provides a description of well known publicly available datasets for energy disaggregation, most of which are either of low frequency (\(\leq\)1 Hz) or of high frequency (\(\geq\)10 KHz), with minimal representative datasets in the range of 10 Hz to 500 Hz. This is why there are not many results for NILM in this frequency range. In the following, we discuss only the literature that is straightforwardly related to the results of this paper. The features are assumed to be chosen beforehand and not extracted automatically by machine learning methods as in [8]. An extensive discussion with respect to different features for energy disaggregation can be found in [5; 20]. Multivariable (many features) approaches have been used for various stages of energy disaggreggation (e.g., see [21; 22] for event detection) aiming at improving effectiveness at the expense of raising the dimensionality of the problem and thus aggravating its efficiency. The impact of using more features for energy disaggregation is usually explored within a proposed framework for solving energy disaggregation or related energy problems. For example, in [23], the authors show how different features improve on the results of the identification of the chosen TV channel based only on the consumption of the TV. In [24], the authors extend matrix factorization to many features (active, reactive, apparent, and current) and show that using more features provide better results than the one-feature approach in [25]. Additionally, the book in [18] contains experiments about the evaluation of features when combined with specific approaches for energy disaggregation. To the best of our knowledge, there are not many results related solely to general feature evaluation for energy disaggregation. In [26] the authors discuss the features that are usually used in NILM up to 2017, and describe an algorithm for selecting the best set of features. In particular, they use Random Forest as a classification method, and apply it on a dataset collected at a 30 KHz sampling rate. They look at power features (e.g., active power, RMS current, etc.) as well as spectral features (e.g., \(j\)-th current harmonic coefficient) both for steady state as well as for transient. They describe an algorithm that automatically selects the feature set that seems to have the best performance. In [27] they evaluate four different machine learning methods for seven different feature sets, showing that Random Forest gives the best results. The datasets they use were collected at 1 Hz and their results show that among the seven different feature sets they use, the best depend on the type of the appliance (resistive/inductive/non-linear). The feature sets consist of statistical features (e.g., mean power) and electrical features (e.g., load angles) showing that the latter contribute more to the differentiation between distinct appliances. The statistical features were generated over a time window of 10 samples (10 s). Additionally, in [18] a preliminary evaluation of certain features is performed on a 44 KHz dataset, leading to the conclusion that the active power and reactive power have quite high differentiation power among many different appliances at this sampling frequency. Finally, as previously mentioned, our goal is to use energy disaggregation methods that have minimal memory and CPU requirements since we would like them to run on the smart meter. Some steps have been taken towards this direction.