Abstract
The active application of photovoltaic for electricity generation could effectively transform neighborhoods and commercial districts into small, localized power plants. This application, however, relies heavily on an accurate estimation of the amount of solar radiation that is available on individual building rooftops. While many solar energy maps exist at higher spatial resolution for concentrated solar energy applications, the data from these maps are not suitable for roof-mounted photovoltaic for several reasons, including lack of data at the appropriate spatial resolution and lack of integration of building-specific characteristics into the models used to generate the maps.
To address this problem, we have developed a modeling framework for estimating solar radiation potentials on individual building rooftops that is suitable for utility-scale applications as well as building-specific applications. The framework uses light detection and ranging (LIDAR) data at approximately 1-meter horizontal resolution and 0.3-meter vertical resolution as input for modeling a large number of buildings quickly. One of the strengths of this framework is the ability to parallelize its implementation. Furthermore, the framework accounts for building specific characteristics, such as roof slope, roof aspect, and shadowing effects, that are critical to roof-mounted photovoltaic systems. The resulting data has helped us to identify the so-called “solar panel sweet spots” on individual building rooftops and obtain accurate statistics of the variation in solar radiation as a function of time of year and geographical location.
