High resolution solar potential computation in large scale urban areas by means of semantic 3D city models

Journal article (2024)

Authors

Longxiang Xu Student
C.A. León Sánchez Urban Data Science - Architecture and the Built Environment
G. Agugiaro Urban Data Science - Architecture and the Built Environment
J.E. Stoter Urban Data Science - Architecture and the Built Environment
Research Group
Urban Data Science (Architecture and the Built Environment) (TU Delft)
More Info
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Published Date

2024

Language

English

Faculty

Architecture and the Built Environment

Department

Urbanism

Research Group

Urban Data Science

Abstract

Solar energy is becoming increasingly important with the transition towards green and sustainable energy. Predicting solar irradiance is one of the core steps to optimise solar energy utilisation when planning and scheduling power grids. Accurate solar irradiance prediction can also help forecast microclimate conditions, enabling the analysis of citizens and planning of optimal intervention strategies for heating or cooling behaviour. This paper discusses a novel approach to computing the solar potential of buildings at the city level with promising scalability using semantic 3D city models. Experiments are conducted at different locations in the Netherlands. We evaluate our results by comparing them to the statistical Dutch data, and CitySim shows huge discrepancies in summer.