An End-to-End Relearning Framework for Building Energy Optimization

Energies, Journal Year: 2025, Volume and Issue: 18(6), P. 1408 - 1408

Published: March 12, 2025

Building HVAC systems face significant challenges in energy optimization due to changing building characteristics and the need balance multiple efficiency objectives. Current approaches are limited: physics-based models expensive inflexible, while data-driven methods require extensive data collection ongoing maintenance. This paper introduces a systematic relearning framework for supervisory control that improves adaptability reducing operational costs. Our approach features Reinforcement Learning controller with self-monitoring adaptation capabilities responds effectively changes operations environmental conditions. We simplify complex hyperparameter process through structured decomposition method implement strategy handle over time. demonstrate our framework’s effectiveness comprehensive testing on testbed, comparing performance against established methods.

Language: Английский

Simulation-Based evaluation of Cost-Responsive supply air temperature control strategy for office buildings across different climates

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115665 - 115665

Published: April 1, 2025

Language: Английский