A central challenge for future energy systems is to coordinate the available energy with local, temporal and quantitative demand. This transition to sustainable systems is putting increasing pressure on politicians, urban planners, energy suppliers and network operators.
In recent decades, research and development (R&D) has made significant progress in the area of building standards and building efficiency as well as in the area of heating, ventilation and air-conditioning systems; breakthroughs are no longer expected. However, the user's participation as well as the utilization of new data and information sources still shows a great potential for energy optimization and planning of buildings, quarters and superordinate energy systems.
The central goal of the project GameOpSys is the development of a mobile application which generates usable data and information for the user's own cost and energy optimization (electricity and heat) by participation via gamification. The combination of this data with
Smart Home applications and the Internet of Things enables the overall goal of crosssectoral energy optimization and improved planning of buildings, neighborhoods and higherlevel energy systems. The transdisciplinary approach of the project has the following innovative content compared to existing concepts and services: (i) The potential of user participation through gamification as well as the utilization of data and information is significantly increased by integrating mathematical and computational methods into the mobile application. While relevant technologies and developments (e. g. PEAKapp) are based on simplified models (e. g. economic time series analyses), the integration of detailed
physical and data-driven models (machine learning) in combination with sophisticated optimization methods has significant advantages: Energy consumption, costs or emissions can be minimized based on the solution of a dynamic optimization problem for the next few
hours and days. Dynamic effects and inertia such as component activation for heating and cooling can be taken into account. The user can - optionally in connection with smart home applications - define setpoints for room temperatures or periods of use for household appliances, for example. The energy supplier has the possibility to influence the process of optimization through incentives and reward systems. (ii) Social-psychological findings of user behaviour are an integral part of the development and (iii) innovative market concepts (blockchain etc.) are taken into account.
The application is implemented for maximum flexibility in terms of its commercial development (app-ready, based on rapid prototyping methods). A fundamental evaluation of the development platform and architecture is also carried out in order to guarantee maximum
flexibility for the planned further development (commercial development after the end of the project).
