# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 2 State of the Art  
### Energy Consumption Prediction  
The study of models for forecasting energy demand is currently the object of research by many authors. By doing a search in Google Scholar with the query \"Energy Demand Forecasting,\" 99,400 works since 2016 are shown. Using the same filters but using the query \"Energy Consumption Forecast,\" the number of works account for 62,400. These numbers can be an indication of the importance of this subject for researchers and society in general. Within energy consumption or energy demand forecasting, buildings have an important role. In some developed countries, the consumption of energy in buildings account for up to 40% of the countries' total energy consumption. Moreover, these values relate directly with greenhouse gas emissions, where, in these same developed countries, greenhouse gas emissions by the building sector accounted for 40% of total emissions [7], thus showing the importance of forecasting energy consumption in buildings as a strategy to decrease greenhouse gas emissions and improve energy efficiency. There are two main approaches for building energy consumption prediction--the physical modelling approach and the data-driven approach [8; 9]. Physical models rely on thermodynamic rules for detailed energy modelling and analysis, whereas data-driven models learn from historical data for prediction. The latter method has gained a lot of research attention in the last few years, as several variables must be taken into account when developing a predictive model that can provide results that are as accurate as possible, like weather conditions or Heating, Ventilation, and Air Conditioning (HVAC) systems [10]. Because of this complexity, the analysis of past data to identify patterns is very  
Figure 1: Proposed approach implemented to change people’s behaviour. important to develop a forecasting model. These patterns can be identified by applying time-series forecasting techniques, as it uses the ordered historical data to identify patterns and use this information to forecast future values. For this work, and considering the data available, the data-driven approach will be the most suitable, where the application of time-series forecasting algorithms will be used to forecast energy consumption. Time-series forecasting techniques have been widely applied to forecast energy consumption, and depending on the data used, each model will be more suitable for that specific problem, as data can be univariate (only one variable is registered) or multi-variate (several variables are analysed as well as their interaction) [10]. For the identification of the predictive models to be applied in this work, several works were reviewed where the application of time-series forecasting models related to energy consumption in buildings provided fairly good results. The AutoRegressive Integrated Moving Average (ARIMA) model and its respective variations depending on the existence of trend and seasonality in data are an example of that. This model was applied in works of [PERSON] and [PERSON] [11], and [PERSON] and [PERSON] [12]. In the first work [11], a comparison between machine learning algorithms for energy consumption forecasting was made with data from a typical three-floor building in Xindian District, New Taipei City in Taiwan. The authors categorized the predictive models into the following three categories: single models (ANN, SVM, ARIMA, etc. ), ensemble models (Bagging, AdaBoost), and hybrid models (ARIMA-ANN, SARIMA-MetaFA-LSSVR). The results provided showed that the hybrid model using SARIMA-MetaFA-LSSVR (a version of the ARIMA that takes into account seasonality) had an RMSE of 0.164 and an MAE of 0.028. The latter work applied the SARIMA model to forecast short-term electric load data, with hourly data from January 2017 to December 2017, where the results obtained were good enough for the authors to consider the SARIMA model as suitable for the problem at hand.