Related papers: Generalizable AI Model for Indoor Temperature Fore…
The use of Earth-Air-Water Heat Exchangers (EAWHE) for sustainable air conditioning has not been widely studied. Due to their experimental nature, methods of characterizing internal thermal air distribution impose high dependence on…
This paper investigates a method to improve buildings' thermal predictive control performance via online identification and excitation (active learning process) that minimally disrupts normal operations. In previous studies we have…
Recently, artificial intelligence-based (AI-based) models for forecasting of global weather have been rapidly developed. Most of the global models are trained on reanalysis datasets with a spatial resolution of 0.25{\deg}*0.25{\deg}.…
This paper presents the predictive accuracy using two-variate meteorological factors, average temperature and average humidity, in neural network algorithms. We analyze result in five learning architectures such as the traditional…
Thermal conditions in the urban canopy exhibit stochastic variability driven by varied radiative fluxes and turbulent wind fields, requiring probabilistic rather than deterministic prediction methods. This study presents a probabilistic…
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Consequently, it enables…
The trustworthiness of AI is considered essential to the adoption and application of AI systems. However, the meaning of trust varies across industry, research and policy spaces. Studies suggest that professionals who develop and use AI…
Understanding the models that characterize the thermal dynamics in a smart building is important for the comfort of its occupants and for its energy optimization. A significant amount of research has attempted to utilize thermodynamics…
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's…
It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for this is to perform temperature scaling, which adjusts the…
Hybrid ventilation is an energy-efficient solution to provide fresh air for most climates, given that it has a reliable control system. To operate such systems optimally, a high-fidelity control-oriented modesl is required. It should enable…
This cross-sectional study investigates how preservice teachers in the Global South engage with Generative Artificial Intelligence across academic and instructional tasks while navigating infrastructural barriers such as limited internet…
This paper presents a household cooling system controller which is adaptive and intelligent in nature. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data namely room temperature,…
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context,…
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a…
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for…
Generative models of complex systems often require post-hoc parameter adjustments to produce useful outputs. For example, energy-based models for protein design are sampled at an artificially low ''temperature'' to generate novel,…
This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025,…
Machine learning is a promising technique for many practical applications. In this perspective, we illustrate the development and application for machine learning. It is indicated that the theories and applications of machine learning…
In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating…