Computing Solar Energy Potential of Buildings Using Airborne LIDAR Data

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Computing Solar Energy Potential of Buildings Using Airborne LIDAR Data

Computing Solar Energy Potential of Buildings Using Airborne LIDAR Data

Ryan Hippenstiel

The objectives of this project are to develop a database and script tool, or at a minimum, a consolidated, documented workflow to compute solar energy potential of buildings using Airborne LIDAR Data. The area to be studied will most likely be within State College Pennsylvania and potentially the University Park campus of Penn State. Upon completion of extraction, an additional product of a database with address information will then be populated with energy data for specific buildings. This will include many smaller objectives to develop the final product. The first step will be to extract building rooftops from the LIDAR data and develop information for each such as area, aspect, and potentially material (if it’s applicable). The roof information will then be used to calculate the potential energy generation and/or savings if an approximate loss can be calculated. A script tool could then be developed to select the rooftops and compute energy values for the dataset. Using address information, the database could be populated with those values for each building.

To complete this work, LIDAR extraction software will be used. There are many available with various features which will be investigated. The selection will be dependent on applicability and access. The LIDAR data for this project will be obtained from the DCNR PAMAP project that has supplied the State of Pennsylvania with vast amounts of orthophotography and LIDAR data.

To complete the database of address, a combination of public data through Centre County and/or the Census Bureau would be used. Geocoding would be completed within ArcGIS.

Once completed, the final product would be an on-demand GIS allowing the user to use single or multiple selection sets to analyze the energy information available. This could potentially lead to further work on energy savings, solar panels, or improvement of existing infrastructure.

My hope would be to present this at the ESRI or PAGIS conference if it becomes a successfully useable tool with potential value to others.

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