Long Term Energy Savings Through User Behaviour Modeling in Smart Homes

• Main Authors: Ferreira, J.C (Author), Mataloto, B. (Author), Resende, R. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: Behavioral sciences; Energy consumption; Home Energy Consumption; IoT; Long-Term Engagement; Monitoring; Real-time systems; Sustainability; Temperature measurement; Temperature sensors; Three-dimensional displays; User Behavior
• Online Access: View Fulltext in Publisher; View in Scopus

 The Internet of Things (IoT) has enabled real-time monitoring of energy consumption in smart homes through sensors embedded in the surrounding environment. In the post-pandemic world, domestic energy management has gained importance due to increased work-from-home consumption, making data collection in a smart home a relevant IoT application with many potential energy savings. However, this information is difficult for most users to understand, and existing monitoring systems’ savings results degrade over time. To address these challenges, this study presents a novel approach for domestic energy consumption, production, and comfort perception using color-based dashboards enhanced for user feedback interaction. The approach includes the management of in-home appliances and comfort levels according to user preferences to attain long-term energy savings. The approach includes multiple appealing strategies such as 3D representation, mobile connectivity, utility integration, and dynamic information, to increase long-term engagement and provides quantitative data on energy savings achieved for one year, where the average energy consumption was reduced by 19%. It was found that the approach sustained user engagement over time, with users actively participating in energy conservation efforts. A community survey with 150 participants was also developed and studied where 72% of the enquired considered our approach more attractive than existing market solutions, and 79% considered it more useful than existing solutions. Regarding the real-time information presented on our approach, 83% of the participants strongly or totally agree that it can change users’ behaviors. Author