# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 1 Introduction  
As of 2020, the building sector is responsible for 35% of the total energy consumption and 38% of the emitted greenhouse gases (GHGs; [1]). In order to achieve our climate goals, significant steps must be made to reduce the energy consumed in the building sector. This reflects on both new construction and the transformation of the current building stock. The European Commission (EC) has recently proposed a revision to the Energy Performance of Buildings Directive [2], with the zero-emission buildings initiative taking a step forward from the nearly-zero-emission buildings one, being a requirement in all EU member countries for all new buildings to be constructed after 2030. Furthermore, in the most recent update of the Directive [3], the EC requires EU countries to develop national long-term renovation strategies that facilitate the cost-effective transformation of their building stock into nearly-zero energy buildings by 2050. Despite these actions and the fact that energy efficiency (EE) has recently improved overall, IEA reports suggest that these achievements remain far from net zero milestones, as the energy intensity of the building sector still needs to drop nearly five times more quickly over the next ten years than it did in the past five to be in line with the Net Zero Emissions by 2050 Scenario [4]. This means that the energy consumed per square meter in 2030 must become 45% less than in 2020. In this context, it becomes evident that EE measures will be crucial towards reducing energy use in buildings and GHG emissions, while maintaining economic growth globally [5, 6]. However, given that retrofits and deep renovations are costly, also involving some sort of uncertainty, it must be ensured that the EE plans and activities to be funded will be able to reduce energy intensity with the optimal allocation of financialresources. Several studies have focused in that direction, trying to determine which EE measures should be financed, mostly by assessing their overall impact from a financier point of view [6; 7; 8; 9]. However, in addition to the financing perspective, there is also the environmental dimension to the EE investment problem, which involves a high level of uncertainty in the estimation of energy savings of an EE renovation action. Simulation tools and physical models are commonly used to estimate the energy savings achieved by renovation actions. However, the drawbacks of such models include their high cost and complexity, also requiring a large amount of data and expert knowledge to implement. As an alternative, data-driven models employing machine learning (ML) or deep learning techniques can estimate energy savings based on previous, similar renovation actions. These models are particularly useful when historical data are available, as they can identify patterns and correlations that may be overlooked by physical models. In this paper, we argue that quantifying the expected savings of various types of EE measures is mandatory for objectively evaluating their potential impact, and we suggest that new technological advances in the field of ML and open data can be exploited to effectively support the decisions of the stakeholders involved in the financing process. To that end, we provide an ML-based methodological framework for a priori predicting the energy savings of EE renovation actions that exploits a mixture of state-of-the-art bagging and boosting prediction algorithms and a database of already funded EE measures. The contributions of our study are summarized as follows:  
* Instead of relying on simulation tools or physical models to estimate the potential impact of EE measures, we propose a data-driven estimation approach that builds on advanced ML ensembling algorithms, namely random forecast, extreme gradient boosting, and light gradient boosting machine.