A new simplified method for estimating solar energy potential in urban environments

In an article published in the scientific journal Nature, researchers from the Delft University of Technology in the Netherlands describe a newly developed method to easily calculate the solar energy that a building can receive in an urban environment.

This new technique is interesting for both architects and engineers, and in the fields of urban planning and property purchasing, as these calculations on the energy potential in photovoltaic systems can be quite complex, depend on several elements and require high ‘computational capacity’. In the current market it can often become a key factor for constructing, selling or renting houses and buildings where aesthetic solutions are becoming more in-demand while also being energy efficient and sustainable. Traditional methods use 3D models of cities (point clouds) and highly accurate data of the buildings and environment to be analysed, including trees, bridges, towers, etc.  In this sense, shadows over time can be calculated, as well as the angles of incidence of sunlight and other details, including the typical weather throughout the year. It is a technique that can be useful if a specific calculation for a particular construction is required (such as a home or a building), yet also complicated to manage when applying to large areas or when rapidly carrying out mass calculations.

 

 

 

This new method involves a model that mathematically simplifies data and takes into account the location and surrounding buildings. Nonetheless, it only requires two factors: the Sky View Factor and the Sun Coverage Factor. It is fundamental to understand that the sun’s movement in the sky varies throughout the year and that solar cell efficiency depends both on the amount of light the panels receive and the angle of incidence.

The mathematical model finds correlations between all this data and it knows which values are suitable within an acceptable margin of error (approximately 10%). Therefore, the model provides formulas with few variables that, in actual fact, result in valid estimations. An example could be to quickly calculate the amount of energy that an electric bicycle solar charging station in a park could generate. If we know the correlation for different parts of the city, we can choose where to install it, based on a map of the region, or see where it would be most efficient. 

Furthermore, the idea can be applied to places with different climates, as only the Sky View Factor values throughout the year, for which historical data can be used, and 3D data of the area to be analysed are necessary. It is all progress in terms of helping the decision-making process for developers and investors when considering installing photovoltaic systems in urban environments and buildings to make better use of solar energy.

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{Photo: Solar panels (CC) Chuttersnap @ Unsplash; Boston Planning & Development Agency}

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