Abstract
In the framework of Urban Building Energy Modeling, several procedures have been proposed to reduce the computational burden to evaluate the thermal behavior of multiple buildings. In a previous work, we presented a novel simplification algorithm capable of abstracting a randomly-shaped building into a representative shoebox. Differently from most of the approaches available in the literature, the proposed method is supposed to go beyond the preliminary assessment of the buildings’ performance in the early design stage or on an annual basis. Indeed, it makes it possible to predict accurately not only the annual or seasonal energy needs but also the hourly thermal load and indoor temperature profiles. In addition to that aspect, the proposed algorithm has been specifically developed to deal with buildings of complex shape and geometry, as they are common in the European building stock, accounting for adjacencies and reciprocal shading effects. After a first validation step carried out at single-building level, the proposed algorithm has been tested on many fictional parametrically-generated neighborhoods and several climates. In such a way, it has been possible to assess the reliability of the algorithm at city scale, with the goal to understand whether the application at urban level, characterized by multiple mutual interactions among buildings, could undermine its accuracy. Furthermore, the role played by solar radiation modelling in the framework of the shoebox simplification and its impact on the outcome of the algorithm have been discussed, presenting two alternatives with different accuracy levels. As a whole, the algorithm showed to be effective in estimating hourly and annual thermal loads and indoor temperatures in district-scale simulations, reducing up to 30 times the simulation time for the considered configurations.