Work

Stochastic optimal control of chiller systems: Data-driven vs. hybrid AI

Year: 2026

Type: article

Abstract: Accurate control of chiller systems is critical for energy efficiency, yet conventional data-driven models often fail under unseen conditions due to poor generalization and unquantified uncertainty. This study proposes a hybrid AI chiller model using transfer learning (TL), combining physics-based knowledge with measured data, and applies it to a stochastic optimal control (SOC) framework. Uncertainty is estimated using a Monte Carlo simulation, allowing risk-aware control decisions. The SOC algorithm minimizes total power consumption by optimizing control variables while making uncertainty-aware decisions that reduce the risk of overcontrol. The hybrid AI model could achieve energy saving by 30.5% and decrease the degree of lower uncertainty compared to a baseline ANN model. These results highlight the value of incorporating both physical consistency and uncertainty quantification in AI-based control, enabling more robust and reliable HVAC operation under real-world variability.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202668908008/pdf

Authors: Sunghyun Kim, Jeeye Mun, Hansol Shin, Joa Hyoung Lee, Yoonmee Doh, Cheol-Soo Park

Institutions: Seoul National University, Electronics and Telecommunications Research Institute, Institute of Construction and Environmental Engineering, Institute of Engineering Research

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green

Funders: Ministry of Trade, Industry and Energy, Ministry of Land, Infrastructure and Transport, Korea Institute of Energy Technology Evaluation and Planning