In recent times, there has been a growing interest in urban building energy modeling
(UBEM), owing to its potential benefits for cities. These benefits include aiding city decision …

Reinforcement learning (RL) has been shown to have the potential for optimal control of
heating, ventilation, and air conditioning (HVAC) systems. Although research on RL-based …

Building energy consumption prediction is an essential foundation for energy supply-
demand regulation. Among them, plug-load energy consumption in buildings accounts for …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

With recent energy and environmental crises, the energy consumption prediction of air
conditioning (AC) is essential. Notably, existing general prediction systems use the air …

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of
the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry …

Calibration of an urban-scale building energy model is crucial for advancing building energy
efficiency codes and policies to reduce carbon dioxide emissions whereas the accuracy of …

We study the problem of tuning the parameters of a room temperature controller to minimize
its energy consumption, subject to the constraint that the daily cumulative thermal discomfort …

Building energy consumption prediction is important for sustainable building and city
construction. However, traditional data-driven prediction methods weakly consider real-time …

Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical
systems solely using operational data. Typically, neural SSMs are trained using data …