## 2 Literature Review
### Energy Simulation Methods: Bottom-Up Approaches  
Bottom-up approaches relate energy consumption to more detailed buildings' characteristics, such as systems and installations, geometry and shape, the thermodynamic properties of construction materials, external climate, indoor set-point temperatures, occupancy and relevant schedules. Since the level of a building's knowledge is highly detailed, the accuracy of the estimate allows for a reliable assessment of the energy demand at the single-building scale. Bottom-up approaches can still be divided into two different categories [5]: statistical methods and engineering methods. Statistical methods identify the relationship between buildings' characteristics and their energy demand, relying on statistical inference. These approaches work similar to top-down strategies, sharing the strength of those techniques in applicability and straightforwardness, even though they use disaggregated data. Statistical methods include regression models, conditional demand analyses and artificial neural networks. Regression analysis is a reliable and fast method to assess the energy demand in buildings. It is based on the determination of the statistical predictors, which are expected to influence the energy consumption of a building. A comprehensive discussion on the use of regression analyses to predict energy consumption in the residential sector can be found in the work of [PERSON] and [PERSON] [16], where the authors compared simple, multiple and multivariate linear regressions against non-linear regression models. Other works studied the complex selection process of predictors (i.e., independent variables), such as in the work of [PERSON] et al. [17] for the Canadian office building stock, the work of [PERSON] et al. [18] for three different climatic zones (Moscow, Bucharest and Nice) and the work of [PERSON] et al. [19] for commercial buildings in the U.S. Conditional demand analysis (CDA) is another statistical technique for modeling buildings' energy demand which performs a regression that splits the energy consumption into contributions given by each end use appliance. In CDA approaches, the energy require ment is expressed as a sum of the energy consumption referring to each of the appliances working in the building. Thus, energy demand is directly related to equipment features, building characteristics or utilization patterns like thermostat settings or occupant behavior. Since the CDA runs a regression based on end uses, information is collected through surveys and utility data. A CDA was first employed in the work of [PERSON] and [PERSON] [20], where the authors presented a set of twelve cross-section regression analyses representing the monthly household demand for electricity in San Diego. Other interesting applications can be found in a study by [PERSON] and [PERSON] [21], where a CDA regression method was applied to electricity consumption in Quebec, Aydinalp-Koksal and Ugursal [22], in which the authors modeled the energy use for Canada at a national-level, and in a study by [PERSON] [23], where the CDA was applied to Japanese household consumption. The artificial neural network (ANN), or simply the neural network (NN), is a mathematical model inspired by the functioning of biological connections between the neurons in animals' brains which grants learning processes. An NN builds a parallel mathematical model based on the interconnected structure of biological neural networks. The NN technique for modeling an individual building's energy consumption originated and evolved in the 1990s. The work of [PERSON] et al. [24] contains one of the first applications of an ANN to forecast the electric load in the Seattle and Tacoma area. Later, [PERSON] et al. [25] developed an NN model to predict the Canadian residential energy consumption. They stated that NN techniques are highly suitable for determining causal relationships among a large number of parameters. More recently, [PERSON] et al.