A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings. (1st November 2021) - Grillone, Benedetto; Mor, Gerard; Danov, Stoyan; Cipriano, Jordi; Sumper, Andreas
Abstract: Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building's weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available. Highlights: An innovative methodology to analyse building energy consumption and estimate energy efficiency savings is introduced. The methodology allows to detail the building energy usage by extracting typical consumption profile patterns. The methodology shows high prediction accuracy while also providing easily interpretable results and actionable insights. The high prediction accuracy is retained even when training data is reduced by 25% of a full operating year.