LAUSR

Abstract In Energy Performance Contracts (EPC), savings resulting from building and energy system retrofit are estimated as the difference between the actual consumption an prediction from a baseline model. This paper proposes an automated method to select the most relevant baseline model. In addition to commonly used cooling degree days (CDD), climatic variables mixing temperatur humidity are introduced, such as dew point or moist air enthalpy. In terms of prediction algorithm, 11 algorithms, linear and nonlinear, are compared. The novel methodology enables an energy analyst to easily find the most relevant model for a specific building, with a focus on both precision and seasonal bias. By applying the novel methodology on 11 buildings under real EPCs, the result shows a decrease of prediction error by 23.5% with respect to models referenced in the contracts. Lastly, the proposed method is generalized to address the case where contracts involve multiple buildings of different types or consumption ranges, with the ability to identify a common best model by using new dimensionless indicators, and reach an 11% improvement in prediction error.