###### Abstract  
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset. energy disaggregation; non-intrusive load monitoring; convolutional neural network; deep learning 2  
themselves, which are divided into _very slow_ (less than 1 min), _slow_ (between 1 min and 1 s), _medium_ (higher than 1 Hz but slower than the fundamental frequency), _high_ (up to 2 kHz) and _very high_ (between 2 and 40 KHz). The features of the _slow_ and _very slow_ category can be used directly, as in [9; 10], through statistical characterization of time-series sub-sequences as well as with signal processing [11; 12; 13]. Higher sampling rates allow for more detailed characterization of transients in the consumption of household appliances [14]. If the sampling rate is high, it is possible to realize a signal transformation like a Fourier transform or a discrete wavelet transform that allow to obtain significant new features on which to base the classification [15; 16; 17]. _Very high_ sampling frequencies allow to obtain information about the load waveform of household appliances, for example through the calculation of voltage-current trajectories as in [18; 19]. _Extremely high_ sampling rates enable to capture a richer set of harmonics as well as the electric noise [20; 21]. Some authors have integrated the features derived from the consumption measurement with other information such as the frequency of use of household appliances [22; 23] or weather conditions [24]. The last step of the disaggregation process consists in identifying the loads from the features. Many different approaches have been proposed in the literature to address this step. A first group uses optimization methods applied to combinatorial search [25; 26; 27; 28], but these methods are limited by the necessary computational resources. The community has therefore focused on both supervised and unsupervised machine learning techniques. Among the supervised techniques, several neural network architectures have been proposed, such as Multi Layer Perceptron (MLP) [29], Convolutional Neural Network (CNN) [30; 31; 32; 33; 34; 35; 36], Recurrent Neural Network (RNN) [30; 37; 38; 39], Extreme Learning Machine [40], techniques based on Support Vector Machines (SVM) [16; 41], K-Nearest Neighbors (kNN) [41; 42] naive Bayes classifiers [15], Random Forest classifier [43] and Conditional Random Fields [44]. Among the unsupervised techniques, it was mainly those based on Hidden Markov Model that were used in this field [26; 28; 45; 46; 47; 48], although clustering techniques were also used [49; 50]. The use of high sampling rates, if on the one hand enables to process signals richer in information, and therefore potentially generate more effective features for disaggregation, on the other hand requires the use of dedicated hardware for the measurement of the total load. The increasing diffusion of smart meters for household users, although typically operating at _low_ or _very low_ sampling frequency, allows to conceive a direct use of the power measurements obtainable from them for the purpose of disaggregation [28].