GameOpSys - Gamification for optimizing the energy consumption of buildings and higher-level systems
Motivation and Research Question
Concepts for future, sustainable energy systems are largely characterized by a radical change of the entire system and thus also its planning and operation: The substitution of a flexibly controllable energy supply by means of fossil power plants by a renewable, partly volatile energy supply, leads to a divergence of supply and demand. A key challenge of future energy systems is to match available energy with demand in terms of location, time, and quantity. This transition to sustainable systems puts increasing pressure on policy makers, urban planners, energy suppliers and grid operators. Research results show that a combination of several, coordinated technologies as well as the coupling of different sectors, the integration of short- and long-term storage, the expansion of transmission capacities as well as the increase of flexibility (supply- & demand-side) is promising.
GameOpSys is particularly concerned with the question of how data can be generated for better planning and optimization, especially with the involvement of the user, and how energy systems can ultimately be optimally controlled.
Initial Situation/Status Quo
In recent decades, research and development in building standards and building efficiency, as well as in heating, ventilation, and air conditioning (HVAC) systems, has made great strides; no more breakthroughs are expected. However, the participation of users and the exploitation of new data and information sources still show great potential for energy optimization and planning of buildings, neighbourhoods, and higher-level energy systems.
Project Content and Objectives
The central goal of the project GameOpSys is the development of a mobile application, which generates usable data and information for energy and cost optimization (electricity and heat) by participation of the user via gamification. The combination of these data with Smart-Home applications and Internet of Things can enable the cross-sectoral energy optimization and improved planning of buildings, districts and higher-level energy systems in the future.
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 harnessing 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. on economic time series analysis), 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 hours and days. Dynamic effects and inertias such as building component activation for heating and cooling can be taken into account. The user can define - optionally in connection with smart home applications - setpoints for room temperatures or operating periods 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 insights of user behaviour are an integral part of the development and (iii) innovative market concepts are considered. In terms of its commercial development, the application is implemented for maximum flexibility (app-ready, based on rapid prototyping methods).
Results and Conclusions
An app-ready solution was implemented based on components such as Flask, MongoDB, and the models developed in GameOpSys, as well as a user interface in the form of an Android application available for download as an alpha version in the Google Play store and operated as a Docker network on a server hosted at the Karl-Franzens University of Graz. Here, the models for predicting the electricity consumption of one's own household provide a new prediction for the next day after receiving intelligently set triggers from MongoDB.
For each household, the historical electricity consumption was visually processed on the basis of uploaded smart meter data and made available in the app. Consumers themselves could enter different activity areas for their behaviour prediction by the hour. The individual electricity consumption predictions were also visualized in the app, allowing users to make comparisons with their previous electricity consumption and to see the effects of different behaviours. The number of days with a complete data picture (smart meter data plus behaviour prediction) were approximately thought of as an Achievement/Score. The wish for more push messages, expressed several times in the final interviews, was very welcome.
The extraction of the smart meter data from the files received after the consumer's opt-in at the respective network operator into MongoDB took place automatically after uploading the file. A complete automation of the data integration was not pursued due to the lack of an interface and the very high effort for web scraping due to different network operators. In addition to the consent forms of the pilot study participants, a data processing agreement was also concluded between the consortium partners. The publication of the subsequently anonymized data as open data is planned after the end of the project. Furthermore, clickworker was used to conduct another large-scale survey on the topic of smart meters. In addition to a descriptive examination of the sample, regression analyses were used to investigate which factors predict the very different attitudes toward smart meters and the willingness to actively use the smart meter.
Different framework conditions in the provision of smart meter data clearly reinforced the fact that a complete data picture per user is relatively difficult to achieve. As an example, not only the different, sometimes somewhat outdated file formats in which the smart meter data is provided, but also the fact that sometimes a new file was started at the beginning of the month without the user noticing. In any case, a standardization of the provision of smart meter data, ideally also via an automatic interface whose use release is subject to the responsible household residents, is a concrete recommendation from the GameOpSys project.
Project or cooperation partners
- Karl-Franzens-Universität Graz / Institute of Psychology
- TU Graz / Institute of Software Technology
- TU Wien / Institute of Energy Systems and Electrical Drives Energy Economics Group