## 1 Introduction  
In recent years, population increase, together with the demands of comfort and the emergence of new technological devices, have been reflected in a rapid intensification in energy consumption. This amount to 39% and 40% of total energy demand and 38% and 36% of carbon dioxide (CO\({}_{2}\)) emissions in the United States and Europe, respectively [1]. That is why forecasting of energy demands in residential buildings is necessary to maximize energy planing, management and resources conservation [2]. Digital progress has also been redesigned in the electrical system through the so-called Smart Grid, which are emerging with the purpose of achieving a more reliable, efficient, safe and sustainable electricity supply, with greater interaction by the consumer [3, 4]. This intelligent system allows data exchange between electricity distribution companies, new service providers and users, by combining information and communication technologies with automation and control, through a more flexible distributed network structure [5]. In addition, cloud computing systems, mobile platforms and sensors of various kinds have made it possible to carry out large scale data analysis and offer real time information, including smart electricity meters to control energy flow [5, 6]. This knowledge related to the energy behavior of users has a double advantage: it offers greater flexibility, business control and personalized marketing strategies to producers and suppliers, while users can adjust and optimize their consumption and reduce costs. The large amounts of data generated by these systems have large storage and analysis requirements. Big Data architectures allows to solve this issue through parallel computing, either in cloud systems or distributed computing. These complex architectures provide powerful tools for companies data storage and management, including high availability (data available at all times), scalability (adjustable resources to ingestion and processing processes), redundancy (to prevent data loss) and partitioning (to increase system performance). These Big Data technologies are combined with artificial intelligence approaches, to maximize knowledge provided by huge amount of data collected, with the purpose of gaining a deeper understanding of household energy consumption and foretell customer's future behaviours. In this regard, the most recent studies related to energy consumption using Machine Learning and artificial intelligence, [PERSON] and [PERSON] [1] found a lack of studies related to residential buildings (only a 19%) which besides had low precision (only 12% used \"sub-hourly\" data). [PERSON] et al. [7] found the highest spatio-temporal resolution when using daily data (cumulative daily consumption and average temperature), although the increase of data spatial resolution improved accuracy. In addition, they reviewed the resolution and type of 24 models recently applied to explain the temperature vs. energy consumption, finding 12 applying linear regression, 4 using nonlinear regression, and three employing mixed models. From them, only 9 used hourly and one sub-hourly data (which included resolutions at device level for individual households), while only 1 considered data at \"sub-city\" level and 9 at city level. Regarding the methodology used for energy demand forecasting, artificial neural networks (ANNs) are becoming very popular, especially for energy planning [8; 9; 10; 11], although many other nonlinear models simpler than ANNs are being used, including decision trees and Random Forest (RF) [12; 10; 13; 14; 15], finding both as viable and accurate alternatives Tso and Yau [16]. For instance, [PERSON] et al. [15] developed a model for energy demand prediction in 80 Japanese buildings using a decision tree with an accuracy of 93% for training data and 92% for test data.