## 1 Introduction
These could be both static information, such as the building location and orientation, building envelope and adopted technology (facades, windows, thermal insulation, heating and cooling systems, lighting systems), and dynamic information, dependent on the use of the building (occupancy profile, lighting profile or heating, ventilation and air conditioning (HVAC) temperature setups). All these types of parameters must be carefully introduced, in order to get a suitable calibration that becomes validated when the simulated consumption strongly matches the actual, measured consumption. A considerable training of the user to deal with the specificities of each simulation software is an additional requirement, often pointed out as a non-negligible challenge. When incorporating exogenous variables, such as weather conditions, a careful sensitivity analysis of the influence of these variables in the energy consumptions must be undertaken, as the simulation takes into account several characteristics that can interfere in these interconnections (as the heat transmission coefficient for different surfaces, or even the effects of shadings or infiltrations that can distort the expected behavior). These relationships are expected to be easily and quickly obtained through the use of data-driven methods anchored on large historical databases, and also online or periodic training could help to follow subtle variations along the building use. With the recent paradigm of accessing high amounts of data in buildings (energy consumptions records and also exogenous variables such as indoor/outdoor temperatures, relative humidity, or solar radiation), it becomes possible to take into account available data to predict future consumptions. In fact, it is the authors' admitted research interest to assess whether machine learning prediction methods to estimate future load demand in buildings may provide an alternative of using common building energy simulation tools. This change in paradigm is illustrated in Figure 1.  
Machine learning approaches, often called data-driven models, require a training stage comparable to the calibration stage in traditional simulation processes, mainly to determine the relative importance of different features used to estimate future load demand. The feature selection stage will be further explored in Section 3. While the training stage occurs, it is expected that estimated consumptions will be closer to real consumptions. When using building energy dynamic simulation tools, sometimes the estimation of energy consumption is far from being coincident with reality when comparing simulations with measured data. The difference between these values is usually designated energy gap [13]. Reducing energy gap is an important issue to be taken into account when designing neutral energy buildings, since small differences in assumptions may result in higher energy performance gaps. Therefore, the use of data-driven approaches appears to be an important alternative to conventional building energy performance simulation tools in reducing the energy performance gap, since they do not rely on assumptions, but on historical data coming from buildings under operation conditions [14]. This study will focus on applying machine learning approaches to forecast thermal energy demand. Different researchers used forecasting models to predict building electricity demand [4, 5, 11, 15]. It becomes clear that, in most cases, it is easier to obtain electrical energy as data with higher resolution, and also that quite often weather conditions can strongly influence these electricity consumptions (mainly during the summer periods, when air conditioning systems are used). Nevertheless, as the building analyzed in this article has its heating and cooling needs provided by district heating (further addressed in Section 2.1), electricity consumptions are quite more stable and easier to predict.