LX
Longxiang Xu
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Currently, approximately 60% of the global population lives in urban areas (UN. Population Division, 2018). Incorrect quantification of the current and expected energy demands of buildings can lead to erroneous decisions and misguided planning for energy supply. Additionally, society is transitioning to adopting more sustainable energy sources to reduce environmental impacts. Solar gains play a major role in energy demand simulations. Therefore, it is important to perform precise calculations of the solar radiation for a given area of interest. However, this energy source faces challenges, such as shadowing, which rapidly decreases the performance of any solar panel, and it is constantly changing owing to the movement of the sun across the sky. Figure 1 shows a sketch of the considerations for computing shadowing calculations and the components of the solar irradiance. [...]
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Currently, approximately 60% of the global population lives in urban areas (UN. Population Division, 2018). Incorrect quantification of the current and expected energy demands of buildings can lead to erroneous decisions and misguided planning for energy supply. Additionally, society is transitioning to adopting more sustainable energy sources to reduce environmental impacts. Solar gains play a major role in energy demand simulations. Therefore, it is important to perform precise calculations of the solar radiation for a given area of interest. However, this energy source faces challenges, such as shadowing, which rapidly decreases the performance of any solar panel, and it is constantly changing owing to the movement of the sun across the sky. Figure 1 shows a sketch of the considerations for computing shadowing calculations and the components of the solar irradiance. [...]
Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact on the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this paper, we present our method to perform shadowing calculations in urban areas using semantic 3D city models, which is split into five sections: Point Grid Generation, Sun-Ray Generation, Nightside Filtering, Bounding Volume Hierarchy and the intersection between the sun rays and the BVH to identify which locations are shadowed at a given moment (epoch). Our tests are performed in Rotterdam’s city center, a dense urban area in The Netherlands. Our initial results indicate that the computational time per 100 k grid points fluctuates within 0.2–0.7s.
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Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact on the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this paper, we present our method to perform shadowing calculations in urban areas using semantic 3D city models, which is split into five sections: Point Grid Generation, Sun-Ray Generation, Nightside Filtering, Bounding Volume Hierarchy and the intersection between the sun rays and the BVH to identify which locations are shadowed at a given moment (epoch). Our tests are performed in Rotterdam’s city center, a dense urban area in The Netherlands. Our initial results indicate that the computational time per 100 k grid points fluctuates within 0.2–0.7s.
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.
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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.