# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 2 Problem Setting and Related Work  
The quantification methods of estimated savings of an EE measure can generally be split in two main categories, namely the a priori (or ex ante) and the a posteriori (or ex post) estimation methods. On the one hand, the a posteriori estimation constitutes the Measurement & Verification (M&V) protocols, in which baseline models are used to determine the energy savings by comparing the energy consumed in the building before and after the implementation of an EE measure. There are several protocols and standards for the M&V process, such as the International Measurement and Verification Protocol [10], the Uniform Methods Project [11] introduced by the US Department of Energy, and the ASHRAE Guideline 14 for measurement of Energy, Demand, and Water Savings [12]. The effort put into modelling in the design phase is not, by itself, a guarantee of optimal measured performance. Optimistic assumptions and simplifications are often made in the design phase and, as a result, validating the simulated results and calibrating the model based on long-term monitoring data becomes critical [13]. The implementation of M&V protocols has been traditionally based on regression models, but recently more and more studies have focused on exploiting novel models and algorithms to further increase accuracy [14; 15]. On the other hand, a priori estimation aims to predict the energy savings before the actual implementation of the EE measure. The ability to have an accurate estimation of the energy savings before an EE measure is critical in order to support investment decisions and drive cost-effective actions overall, as energy consumption attributes of buildings are more important than financial and social ones for their investment evaluation [16]. This is further elaborated, as it is documented that the inability to accurately evaluate the impact of EE measures in buildings through robust methodologies can slow their adoption [17]. Focusing on a priori estimation, data driven methods are becoming more popular due to the increased availability of data from sub-metering measurements in buildings [18] and to the evolution of data-driven techniques and ML algorithms that can process all these data and turn them into valuable insights [19]. Thus, several studies have been proposed to support the identification of the optimal EE measures in building typologies and their subsystems, using either simulated [20; 21; 22] or real [23; 24] data and exploiting different types of models, such as neural networks [24; 25; 26], support vector machines [20; 23], or ensembling models [22]. Although they do not directly predict the expected savings, the above-mentioned methods could be used in a priori approaches for estimating the energy savings of an EE measure. Moreover, several studies have focused on proposing data-driven frameworks to directly calculate energy savings before the actual implementation of an EE measure. A fallen rule list classifier to predict the reaction of buildings in several types of EE measures has been proposed by [27], to increase the impact of energy audits by reducing the cost and complexity of energy retrofit processes. The study focused on over 1000 buildings in New York City and analyzed audit records of the buildings to identify opportunities for EE measures across a range of system categories (heating, cooling, domestic hot water, ventilation, lighting, and building envelope details). Another study developed by [28] in the Norwegian retail food market tested the accuracy of two statistical learning models, Broken Lines and Tao Vanilla Benchmarking method, to estimate energy savings in five retail stores through retrofitting. The granularity of the estimations was performed on an hourly level (Tao Vanilla method) and a weekly level (Broken Lines model), with the latter being easier to analyze in terms of the effects in energy consumption caused by weather conditions.