Results from the monitoring of indoor environment and occupant perceived productivity in office buildings

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Buildings are designed to respond to functional and regulatory needs, providing comfortable conditions to occupants, offering satisfactory environmental settings, minimising health risks, and enhancing individual and collective quality of life. Although th ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigat ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

Given people's significant time spent indoors, ensuring good indoor air quality (IAQ) is essential because it significantly influences occupants' health and productivity. Office buildings consume about 50% of commercial building energy and 18% of total bui ...

As humans spend most of their time indoors, indoor air quality (IAQ) significantly impacts their health. In parallel, building ventilation consumes significant energy, contributing to climate change. However, the relationships between the building ventilat ...

Smart thermostats are increasingly popular in homes and buildings as they improve occupant comfort, lower energy use in heating and cooling systems, and reduce utility bills by automatically adjusting room temperature according to measurements of their bui ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Residential ventilative cooling via natural ventilation is influenced by outdoor air pollution. However, relative to climate, outdoor air pollution is not comprehensively considered in determining the ventilative cooling potential of buildings. To assess t ...

Currently, the indoor thermal environment in many buildings is controlled by conventional control techniques that maintain the indoor temperature within a prescribed deadband. The latest research provides evidence that more dynamic variations of the indoor ...

Professionals in the building design and operation fields typically look at standards and guidelines as a reliable source of information and guidance with regard to procedural, contractual, and legal scope and requirements that are relevant to accountabili ...

Background: A neurocognitive phenotype of post-COVID-19 infection has recently been described that is characterized by a lack of awareness of memory impairment (i.e., anosognosia), altered functional connectivity in the brain's default mode and limbic netw ...

Skyscrapers or glass towers are an extremely common model throughout the world. In the current context of climate change and resource depletion, we need to develop new postures towards these objects, especially for existing buildings that are becoming obso ...

Heating, Ventilation, and Air Conditioning (HVAC) Systems utilize much energy, accounting for 40% of total building energy use. The temperatures in buildings are commonly held within narrow limits, leading to higher energy use. Measurements from office bui ...

Results from the monitoring of indoor environment and occupant perceived productivity in office buildings

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Random matrix methods for high-dimensional machine learning models

What is NExT? A new conceptual model for comfort, satisfaction, health, and well-being in buildings

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

On distributional autoregression and iterated transportation

Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment

Iterative removal of sources to model the turbulent electromotive force

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

Assessment of Monitoring Strategies for Inhalation Exposure and Occupancy in Office Environments

Building ventilation and outdoor air pollution: Implications for health, comfort and energy

Efficacy of temporal and spatial abstraction for training accurate machine learning models: A case study in smart thermostats

Distributional Regression and Autoregression via Optimal Transport

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Influence of outdoor air pollution on European residential ventilative cooling potential

Dynamic indoor thermal environment using Reinforcement Learning-based controls: Opportunities and challenges

Thermal Conditions in Indoor Environments: Exploring the Reasoning behind Standard-Based Recommendations

Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

Taking a step back from glass towers facades to make them compatible with the 2050 targets

DIET Controller: Dynamic Indoor Environment using Deep Reinforcement Learning