Michael Rossetti
Michael Rossetti is a data scientist, software developer, and machine learning researcher. He has worked as a polling data analyst for a winning US Presidential campaign, a data analytics director for a Silicon Valley startup, and a technology consultant for the US Government. He teaches courses in data science, computer science, and software development, and conducts research in applied machine learning.
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Building energy simulation is a critical tool for developing and
testing advanced control strategies, such as Reinforcement Learn-
ing (RL), to provide demand flexibility and affordable energy costs.
The recently introduced Smart Buildings Control Suite (sbsim)
provides a lightweight, scalable, and data-calibrated simulation
environment based on a 2D finite-difference model. However, the
initial model primarily focused on conductive and convective heat
transfer, neglecting the significant impact of long-wave radiative
heat exchange between interior surfaces. This paper presents a
significant extension to the sbsim framework by incorporating a
physically-grounded model for interior radiative heat transfer. Our
primary contribution is the development and integration of a fully
tensorized radiative heat transfer module, which preserves the com-
putational efficiency and scalability of the original simulator. This
was achieved by developing a pipeline for view factor calculation,
including an algorithm to identify directly seeing surfaces within
complex floor plans, and formulating the net radiation equations
for efficient execution on modern hardware accelerators. We val-
idate the numerical accuracy of our tensorized implementation
by comparing its results against a traditional iterative approach,
demonstrating identical outcomes. This enhancement increases the
physical fidelity of sbsim, enabling more accurate training of RL
agents for building energy optimization.
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