In-House Energy Data Visualization to Create Energy Consumption Awareness

Abstract

This thesis presents the design and implementation of the Energy Visualization Management (EVM), a desktop-based prototype aimed at fostering energy awareness through personalized, profile-driven feedback in simulated smart home environments. Addressing the central research question “How can a system visualize in-house energy usage to create energy awareness and support the personal needs and interests of different user profiles across various screen sizes and devices?”, the study draws from principles of energy informatics, and human-centered design (HCD). The conceptual framework introduces a multi-profile, multi-device strategy to reflect the diverse cognitive needs, motivational drivers, and technological proficiencies of typical household members. User personas, including a data-driven father, an environmentally conscious mother, a competitive teenage son, and a curious young daughter, are mapped to adaptive feedback experiences tailored to their roles and preferred devices (e.g., desktop, tablet, smartphone, tabletop display). Implemented using C#, WPF, SQLite, and LiveCharts, the EVM system simulates daily routines, energy consumption, and production scenarios without requiring physical sensor data. Visualizations are rendered dynamically using profile-specific logic and include interactive charts, ambient metaphors, gamified avatars, and near real-time notifications. Feedback is delivered through both quantitative and symbolic means, enabling intuitive engagement even for users with limited technical background. A modular MVVM architecture enables feedback loops and supports interface adaptation across user types. While the EVM prototype remains a simulated system, it illustrates how personalized, user-centered design approaches may support engaging and accessible energy feedback in household contexts. The implementation provides a conceptual and technical foundation for future research in energy visualization, with potential for extension into real-world IoT applications and behavioral studies.

Date of Award	2025
Original language	English
Supervisor	Jens Krösche (Supervisor)