## 2 Literature Review
### Energy Simulation Methods: Top-Down Approaches  
Top-down approaches analyse a large group of buildings as if they were one single unit and study the interactions between energy use and the economy, population, technology or climate. A huge number of external factors are, in fact, significant in the determination of energy consumption and production. Such factors include, to name a few, demographic growth, macroeconomic indicators, market, construction and demolition rates, policy transformations, energy price, weather, consumer preferences and technological innovation [6]. In other words, top-down strategies capture the relationship between energy use and its major drivers, which is what makes these approaches extremely useful for estimating energy consumption at an urban scale, when it is crucial to determine the effects on the energy demand due to ongoing long-term changes. Top-down models are based on statistical inference, and they can be categorized according to the variables analyzed (e.g., economic, technology, physical or mixed variables). _Econometric models_ are primarily based on prices and incomes. Economic variables may comprehend social or economic conditions, and they capture the effects of policies and market trends on energy use. _Technological models_ link energy consumption to the widespread characteristics of the stock, while _physical models_ rely on climate, weather and temperatures. One of the first applications of a top-down approach can be found in [PERSON] et al. [7], where the authors developed an econometric model to assess residential energy use over time. Their model was sensitive to changes in technology, population and the economy. Other top-down approaches for energy consumption estimation have been developed for several countries, such as the USA, Japan, Sweden, Germany, the UK [8; 9; 10; 11], Norway [12], Denmark [13], China, Canada, Spain [12], Turkey [14] and New Zealand [15].  
Top-down models only use aggregate data, which is both a strength and a weakness. On the one hand, aggregate data are easier to collect, and no detailed analysis of buildings is required. Moreover, they are able to include historical information, producing models very reliable for describing aggregated trends for future predictions in a stationary market, capturing the long-term effects of macroeconomic phenomena on energy demand. On the other hand, they do not provide appropriate predictions if significant changes happen in the economy, technological innovation or climate. Historical data give inertia to the model, and significant errors may occur because social, economic and weather conditions are likely to change over time. Most importantly, top-down methods cannot give sufficient knowledge about single buildings or single energy end uses, and they can only represent energy consumptions at an aggregate level.  
To sum up, top-down methodologies are preferred when the objective is to underline the connection between energy use and different socioeconomic aspects. As much as those techniques are very useful for urban, regional and national planning and large-scale modeling, the building designer cannot rely on them for identifying specific areas to be enhanced or for scheduling energy efficiency interventions if our analysis works at a portfolio level.