[Service_Parameters: Service_Topic='DATA ANALYSIS AND VISUALIZATION', Service_Term='GEOGRAPHIC INFORMATION SYSTEMS', Service_Specific_Name='WEB-BASED GEOGRAPHIC INFORMATION SYSTEMS']
Mapdwell Solar System Rooftop Solar Mapping ToolEntry ID: Mapdwell
Abstract: Solar System is an interactive online rooftop solar mapping tool designed, engineered, and developed by Mapdwell. Solar System allows users to precisely estimate rooftop solar electric potential (PV panels) of every building in a given city and translates the results into metrics that inform a decision. The tool is currently available for Cambridge, MA, Washington, D.C., and Wellfleet, MA.
For ... the D.C. sample, Solar System maps the solar potential of over 160,000 buildings, identifying high yielding solar resources for over 2.5 gigawatts of potential solar photovoltaic installations and over $10 billion in local business. It also pinpoints more than 800 existing installations equivalent to ~4 Mega Watts of installed capacity.
Mapdwell Solar System employs a rich framework for the processing of LiDAR data, the computation of solar radiation, and the purveyance of meaningful and relatable information to consumers regarding their solar potential. The framework is built upon the familiar and intuitive Google Maps API as a rich web map service, providing this information in objective metrics such as system size in kWh as well as relatable measures like the number of trees saved by installing a solar system.
Purpose: Mapdwell Solar System advances proprietary, industry-leading technology to enable the:
-Determination of slope, shape, and orientation of building rooftops;
-Simulation of solar irradiation taking into account historical weather data;
-Consideration of physical obstructions like vegetation and surrounding buildings;
-Computation of potential solar power generation;
-Application of national, state, and local utility rates and incentive programs;
-Delivery of accurate and unbiased information in a beautiful, user-friendly interface.
Quality Validation testing has shown that our technology yields results with a ±3-5% margin of error.
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