## 1 Introduction  
Energy management plays an important role in buildings nowadays, and its interest is still rising [1, 2, 3, 4, 5], as more and different types of sensors are being installed in existent buildings, or form part of projects when designing new buildings. Automatic meter readings are now installed in key locations of many new and retrofitted buildings, collecting information for intelligent decision making, providing different types of energy disaggregation in different building areas, and enabling access to high resolution data (half-hourly, quarter-hourly, or possibly even smaller periods). In order to take advantage of available consumption data being continuously accessed through the Building Management Systems (BMS), new strategies should be designed to better analyze the incoming data [6]. These types of analyses could be providential to better accompany consumptions trends, to project the effect of demand-side actions and even to rapidly identify untypical consumption patterns (operation alarmistic purposes) [1, 5]. The accomplishment of the targets and requirements towards the implementation of neutral or positive energy buildings will also benefit from the analysis of data gathered from existing buildings to improve their design and operation [7]. The ambition of achieving energy neutrality of a building goes beyond the improvements of energy efficient use. Energy neutrality requires that the efforts towards a better use of energy resources are complemented with the possibility of in situ renewable primary energy conversion [8]. Feasibility studies concerning the optimized operation of renewable energy generation and efficient use of resources, including economic constraints, are needed for adequate design of nearly zero energy buildings. Often, this type of analysis requires significant computation resources, which could consequently led to long computation times, thus data-driven approaches play an important role in predicting buildings' energy performance [9; 10]. Mathematical models have been proposed for different applications, such as load forecasting, load profiling, load disaggregation, and anomalous consumption detection. Regarding forecasting purposes, the models can be divided into more conventional statistical approaches (linear, polynomial, and autoregressive integrated moving average (ARIMA) methods) or the latest and popular approaches based on machine learning (such as artificial neural networks and support vector regression) [4; 5; 11; 12]. It is widely recognized that the use of building energy performance simulation software (such as _EnergyPlus_) is widely disseminated [2; 4], mainly to obtain an estimate of the consumption when buildings are being designed. With accurate parametrization, it is then possible to infer the influence of different passive elements (walls, glazing, insulation materials, natural ventilation...) and their features in the building's energy consumptions. Furthermore, it is also quite relevant to evaluate the impact of the choice of technology for active elements (cooling and heating systems, water heating systems, lighting systems, etc.) in energy vector consumptions, as well. Building energy simulation tools can also be used when the buildings are being operated, in order to enable building benchmark comparisons to other, similar buildings, or to compare adopted parameter setups and setpoints (e.g., temperature), to support energy certification processes, or even to evaluate energy savings and environmental benefits to be obtained through demand-side actions for projected scenarios [1]. One of the main constraints when applying these simulation tools to existent buildings is the calibration process, which mainly consists of suitable parametrization of the simulation inputs.