The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale

Xavier Faure,Tim Johansson,Oleksii Pasichnyi

doi:10.3390/en15041525

Abstract

New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.

Highlights

Buildings are responsible for one-third of the total final energy use and nearly 40%of total greenhouse gas emissions [1]
The results of parametric simulations are presented to analyse the impact of the level of detail (LoD) (Section 4.1, the thermal zoning impact (Section 4.2), and impact of the level of detail (LoD) (Section 4.1, the thermal zoning impact (Section 4.2), the shadowing impact of the surrounding environment (Section 4.3)
This paper has presented MUBES—a new simulation tool for urban building energy modelling (UBEM)

Summary

Introduction

Buildings are responsible for one-third of the total final energy use and nearly 40%. Of total greenhouse gas emissions [1]. This sector is one of the key contributors to climate change and should be addressed in order to meet the 1.5 ◦ C scenario [2]. The current speed of building energy transition is much slower than what is needed to meet national and local climate commitments [3]. New decision-making paradigms and tools are required to improve the overall efficiency of the building sector. There is an urgent need for integrated models and tools that would allow for the assessment of the benefits and deficiencies of each urban energy intervention in a holistic manner for all of involved stakeholders.

Objectives

Results

Conclusion