Saving energy and money is hard. Too many questions around the financial profitability, technical requirements and legal issues of renewable energy or energy conservation need to answered. The current web offering to find saving solutions are not of great help, because they are lengthy and their usage is tiring.

A solution is the renooble project, which is determining the optimal energy technology for homeowners based on only one information, the users address. The project is based of an ecosystem of Python, Django, PostgreSQL/PostGIS and various packages (GeoDjango, Celery, South, etc.). The project’s search algorithm, implemented in Python, combines various input APIs (weather data, real estate information, govt’ incentive information, etc.) and uses the received information for the decision-making. The results are displayed through the Django framework in a web application. The poster talk will demonstrate how the ecosystem is used to determine the best savings advice for the user and how we tackle the complexity of the decision-making. Furthermore, the we demonstrate how the search algorithm is implemented and how the implementation is benchmarked. Through Python and its frameworks, we are able to help people to save energy and money.