Our research in Smart Systems focuses on the design and optimization of intelligent infrastructures that integrate energy, computation, and connectivity. We develop models and algorithms that enable adaptive control and efficient resource management in real-world environments. Key areas include:
Smart Energy Systems: We investigate optimization techniques for energy flow management in systems combining renewable sources, energy storage, and grid interaction. This includes demand response strategies and intelligent control in home and grid-level energy systems.
IoT-Enabled Control Systems: We explore the use of IoT technologies to monitor, analyze, and optimize distributed systems in real time, enhancing automation and situational awareness.
Optimization and Learning: We apply machine learning and evolutionary optimization methods to solve complex multi-objective problems in energy and infrastructure management.
Our goal is to create scalable, adaptive, and sustainable systems that support the vision of smart homes, smart grids, and interconnected environments.
Multi-objective optimal power flow of thermal-wind-solar power system using an adaptive geometry estimation based multi-objective differential evolution
Truong Hoang Bao Huy, Hien Thanh Doan, Dieu Ngoc Vo, Kyu-haeng Lee, and Daehee Kim
ELSEVIER Applied Soft Computing, 2023
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization
Huy Truong Dinh, Kyu-haeng Lee, and Daehee Kim
Elsevier Applied Energy, 2022
A Home Energy Management System with Renewable Energy and Energy Storage Utilizing Main Grid and Electricity Selling
Huy Truong Dinh, Jaeseok Yun, Dong Min Kim, Kyu-haeng Lee, and Daehee Kim
IEEE Access, 2020