Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method

This paper aims at indicating and certifying the implemented framework for forecasting buildings' energy demand of the city of Bologna, Italy. The method is developed through an automated calibration and is based on 7 known, physics-based building parameters and 6 unknown, and highly uncertain variables. The proposed method focuses on reducing computing time while keeping the accuracy of the output by narrowing the uncertainties in predicting unknown parameters. To accomplish this task, 11 archetypes are defined which are representatives of the buildings in a specific neighborhood in Bologna, Italy. For every defined archetype, the most informative unknown variables are recognized and the Gaussian Process (GP) is employed to emulate the variable-to-data map. A wide sampling of the GP outputs is then applied by No-U-Turn Sampler (NUTS). The methodology is validated for 1156 Italian urban buildings based on the city database. The level of evaluation metrics demonstrates no bias in the output of the long-term forecasting while it accelerated the prediction of building energy demand and calibration on the city scale. The method is flexible for application in other contexts and various available urban datasets. •This paper proposes an archetype coding method for simplifying buildings classification on large scales.•The energy demand of 1156 buildings in the city of Bologna, Italy is forecasted by the Bayesian calibration method.•The method prevents monotheism in applying retrofitting strategies for various types of buildings on large scales.•The model has proven the accuracy in forecasting energy demand and also estimating unknown parameters in urban buildings.