Coopterr, Rennes Métropole's territorial cooperation platform, is now online. Following the launch of the first service "Waves and Measurements," the Solar Cadastre application now enables citizens to assess the photovoltaic potential of their homes in a few simple steps. 

Camptocamp put its expertise to work for the city, by developing a totally customized application, based on an extremely clear user experience. By entering an address, users can visualize their roof in 3D, equipped with photovoltaic panels, and estimate the amount of electricity they can produce using solar energy. It also allows them to explore energy-saving opportunities. To help make solar panel installation projects a reality, a number of contacts are provided for certified professionals, as well as referenced sites in the field.

A User-Centered Approach

The application's design is based on a user-centered approach, where active participation of users themselves plays an essential role. Through over twenty workshops organized, we collaborated closely with project leaders specialized in renewable energies. 

These exchanges helped understand the users' actual needs and design a smooth and intuitive user journey. Moreover, user tests were conducted to continually adjust and improve the user experience, making solar energy production simulation as accessible and understandable as possible.

A Synergy of Complex Geodata

The Solar Cadastre application leverages various data sources. The integration challenge lies in the balanced combination of 2D and 3D data to avoid visual overload and information overload.

Among others, the ENEDIS API is used to retrieve electricity consumption and production data at the iris scale, while Rennes Métropole's RVA (Roads and Addresses Repository) and Sites and Organizations APIs are integrated for address search functionality.

Rennes Métropole provides data on public solar installations, solar potential data, base maps, 3D building tiles with irradiance textures, and terrain.

Each layer serves specific purposes, such as address bar data providing coordinates and 3D buildings providing information like roof orientation and slope, indicating the most favorable part for solar panel installation.

Using all these elements, we utilize the Autocalsol API to retrieve climate data and return energy production, comparing it to annual consumption for a comprehensive understanding. This information is summarized in a downloadable PDF report. 

Realistic Optimization of Solar Panel Placement

We calculate and position the number of solar panels by cross-referencing multiple data sets.

Firstly, we use roof footprint to perfectly position the obstacle grid, considering gutter orientation and roof shape. Users identify roof obstacles through a 5 cm orthophoto. 

By combining these obstacles with the best-exposed areas of the roof, we determine the optimal location for maximum solar panels.

A User-Centric Software Architecture

In the solar use case, the software architecture follows a similar approach to the wave use case but with specific adjustments to meet the requirements of guided user journeys and additional features such as email sending, PDF generation, and the use of external APIs.

Consequently, this architecture includes a backend to manage progressive information collection and implementation of previously mentioned advanced features.

Similar to the wave use case, we utilize the open-source JavaScript framework and API VCMap. This enables us to build dynamic and interactive web maps, displaying 2D and 3D data from various sources and interacting with them effectively.

However, in the solar use case, by limiting ourselves to the CORE of the VCMap solution, we were able to integrate design elements and many other tools. Among these, we developed an algorithm for automatic placement of solar panels, considering roof shape and best-exposed areas. Thus, the simulation begins with optimal and realistic solar panel placement, offering a guided and precise approach for users.

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