4 papers
Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
Shadmehr Zaregarizi, Khashayar Yavari
Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest uncertainty estimates. We p…
PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management
Shadmehr Zaregarizi, Khashayar Yavari
Occupant comfort and grid-aware energy efficiency are competing objectives whose joint optimization depends critically on how reward functions are specified in deep reinforcement l…
OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings
Shadmehr Zaregarizi, Khashayar Yavari
Large language models (LLMs) have demonstrated promising capability in generating reward functions for deep reinforcement learning (DRL)-based building energy management. However,…
Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting
Shadmehr Zaregarizi, Khashayar Yavari
We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark, which evaluates machine learning models across twelve distinct tasks spanning five qual…