# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 4 Case Study
### Data Set  
The case study of the proposed methodology has been performed using EE investment data from various sources, such as the De-risking Energy Efficiency Platform (DEEP) database. DEEP is an open, online database including insights for many EE investment projects aspiring to enhance performance monitoring and benchmarking. Through such indicators and statistics, these databases offer stakeholders the opportunity to better understand the real benefits and risks of EE investments though market evidence and track records of thousands of EE projects from both buildings and the industry. The available data include the type and category of the EE project, the country and sector on which the project has been applied (building or industry), the projected (a priori) net annual savings, the cost of the investment, as well as several financial indicators such as the payback period, the internal rate of return, and the net present value of the investment. Moreover, the fact that the data used are open and follow the FAIR (findable, accessible, interoperable, and reusable) principles provides many benefits to the scientific community, including increased transparency, reproducibility, and collaboration, ultimately leading to better solutions to complex problems [55]. The selection of the most suitable features for predicting the energy savings of EE renovation projects was based on previous studies [16,56,57] but also performed with respect to the available data. Therefore, the selected input features are the following:  
* _Project sector_, namely whether the investment refers to a building or the industry. * _Investment cost_, measured in Euros. * _Projected net annual savings_, measured in Euros, as estimated at the time the investment was made. * _Country_ where the EE project was applied. * _Investment category_, indicating the main target of the EE renovation project in terms of energy usage or building fabric (e.g., heating, cooling, lighting, pumps, or building fabric). * _Investment type_, indicating the nature of the EE renovation project in more detail compared to the investment category (e.g., technical versus operational optimization when it comes to pump or lighting optimization). The data set consists of a fully anonymized sample of 4183 EE investments from 9 countries (Belgium, Bulgaria, Denmark, France, Germany, Latvia, Sweden, the United Kingdom, and the United States). This set of investments has been carefully selected, excluding EE projects with missing data in one or multiple features. From the total number of 4183 EE projects with available data, 412 projects have been ruled out as outliers, and the remaining 3771 form the final data set. The existence of outliers was pointed out by the use of distribution box plots, and the method followed to identify potential outliersin this specific dataset is based on the extraction of the top and bottom percentiles using the ratio of cost to energy savings. More specifically, any data point that falls outside of the range defined by the 5 th and 95 th percentiles of the datasets have been considered as outliers, in order to rule out unrealistic records subject to human error in the process of collecting the information. The most demanding pre-processing step, though, has been the formulation of investment categories and investment types in a mutually exclusive and collectively exhaustive way. As seen in Table 1, there are a total of 8 EE investment categories, consisting of 27 discrete investment types. Given the inherent structural and technical differences of each type of investment, a high degree of variability is expected among the considered categories (or even types of of investment), both in terms of investment cost and energy savings achieved by the respective EE actions. A representative visualization of these differences among the investment categories is presented in Figure 3. The box plots of Figure 3 provide a summary of each feature, including the minimum score, first quartile, median, third quartile, and maximum score.