Energy and Data Science
In the nexus of energy and data science, energy behavior emerges as a cornerstone for shaping sustainable futures. Energy behavior can be defined as the entirety of human actions that influence how energy is harnessed and consumed, encompassing everything from our choice of energy-efficient technologies to our daily consumption habits and the cognitive processes that drive these decisions. Data science plays a pivotal role in understanding these behaviors by extracting intricate patterns from an avalanche of data sourced from smartphones, wearables, and energy consumption metrics and providing granular insights into energy consumption habits. We develop technologies that employ digital phenotyping to discern energy consumers' behavioral patterns, inducing energy-saving habits among users. We aim to usher in an energy-efficient lifestyle that stands in harmony with our global aspirations for carbon neutrality.
1. EV Departure Time Prediction for BMS algorithm
EV (Electric Vehicle) face challenges of battery degradation as the capacity diminishes with successive charge and discharge cycles, leading to reduced vehicle range. To address this, we develop an AI-driven BMS (Battery Management System) algorithm that predicts EV unplug time, to minimize the duration batteries remain at full charge. We base our research on the idea that the unplug time is determined by departure time of EV, which is inherently tied to human behavior. By utilizing lifelogging data through digital phenotyping, we capture the intricate aspects of departure behaviors across variable scenarios. We hope to reduce replacement and social costs related to waste battery disposal, and accelerate the adoption of battery-based vehicles and energy storage systems.
2. Quantified-self For Energy Efficient Lifestyle
Household energy consumption is on the rise globally, with Europe and the U.S. seeing residential sectors accounting for about 30% of their total energy usage. Similarly, in China, households are now responsible for 11% of the nation's energy consumption. This growing demand intensifies global climate challenges and escalates carbon emissions. Addressing these issues requires boosting energy efficiency and managing energy demand concurrently. In our research, we focus on developing foundational technology to model energy consumption behavior by utilizing life-logging data from mobile devices and smart meters. Our study can provide personalized energy-saving strategies based on individual energy consumption patterns.
Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2023 ACM International Symposium on Wearable Computers
- Poster: Departure Time Prediction Using Smartphone Data for Delayed-Full Charging BMS Algorithm
- Yonggeon Lee, Woojin Song, Juhyun Song, Youngtae Noh
- PhD Student
- Kobiljon Toshnazarov
- Stress Detection
- mHealth Data Collection
- Digital Therapeutics (DTx)
- Integrated PhD Student
- Lee Yong geon
- Sensor Data Science
- Energy Behavior Modeling
- Energy Management System
- Digital Therapeutics