Novel Physics-Based Machine-Learning Models for Indoor Air Quality
Approximations
- URL: http://arxiv.org/abs/2308.01438v1
- Date: Wed, 2 Aug 2023 21:22:17 GMT
- Title: Novel Physics-Based Machine-Learning Models for Indoor Air Quality
Approximations
- Authors: Ahmad Mohammadshirazi, Aida Nadafian, Amin Karimi Monsefi, Mohammad H.
Rafiei, Rajiv Ramnath
- Abstract summary: Machine learning models are capable of performing air-quality "ahead-of-time" approximations.
In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations.
The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models.
- Score: 0.5249805590164902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cost-effective sensors are capable of real-time capturing a variety of air
quality-related modalities from different pollutant concentrations to
indoor/outdoor humidity and temperature. Machine learning (ML) models are
capable of performing air-quality "ahead-of-time" approximations. Undoubtedly,
accurate indoor air quality approximation significantly helps provide a healthy
indoor environment, optimize associated energy consumption, and offer human
comfort. However, it is crucial to design an ML architecture to capture the
domain knowledge, so-called problem physics. In this study, we propose six
novel physics-based ML models for accurate indoor pollutant concentration
approximations. The proposed models include an adroit combination of
state-space concepts in physics, Gated Recurrent Units, and Decomposition
techniques. The proposed models were illustrated using data collected from five
offices in a commercial building in California. The proposed models are shown
to be less complex, computationally more efficient, and more accurate than
similar state-of-the-art transformer-based models. The superiority of the
proposed models is due to their relatively light architecture (computational
efficiency) and, more importantly, their ability to capture the underlying
highly nonlinear patterns embedded in the often contaminated sensor-collected
indoor air quality temporal data.
Related papers
- Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator [15.313871831214902]
The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations given on-orbit thermal load conditions.
We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft.
The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.
arXiv Detail & Related papers (2024-07-08T16:38:52Z) - Generating Synthetic Net Load Data with Physics-informed Diffusion Model [0.8848340429852071]
A conditional denoising neural network is designed to jointly train the parameters of the transition kernel of the diffusion model.
A comprehensive set of evaluation metrics is used to assess the accuracy and diversity of the generated synthetic net load data.
arXiv Detail & Related papers (2024-06-04T02:50:19Z) - Physics-Driven Turbulence Image Restoration with Stochastic Refinement [80.79900297089176]
Image distortion by atmospheric turbulence is a critical problem in long-range optical imaging systems.
Fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions.
This paper proposes the Physics-integrated Restoration Network (PiRN) to help the network to disentangle theity from the degradation and the underlying image.
arXiv Detail & Related papers (2023-07-20T05:49:21Z) - Inference from Real-World Sparse Measurements [21.194357028394226]
Real-world problems often involve complex and unstructured sets of measurements, which occur when sensors are sparsely placed in either space or time.
Deep learning architectures capable of processing sets of measurements with positions varying from set to set and extracting readouts anywhere are methodologically difficult.
We propose an attention-based model focused on applicability and practical robustness, with two key design contributions.
arXiv Detail & Related papers (2022-10-20T13:42:20Z) - Physics-informed machine learning with differentiable programming for
heterogeneous underground reservoir pressure management [64.17887333976593]
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection.
Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface.
We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
arXiv Detail & Related papers (2022-06-21T20:38:13Z) - Physics-informed linear regression is a competitive approach compared to
Machine Learning methods in building MPC [0.8135412538980287]
We show that control in general leads to satisfactory reductions in heating and cooling energy compared to the building's baseline controller.
We also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models.
arXiv Detail & Related papers (2021-10-29T16:56:05Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z) - Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently.
There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance.
We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
arXiv Detail & Related papers (2021-06-17T00:49:55Z) - Quaternion Factorization Machines: A Lightweight Solution to Intricate
Feature Interaction Modelling [76.89779231460193]
factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
We propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM) for sparse predictive analytics.
arXiv Detail & Related papers (2021-04-05T00:02:36Z) - Physics-Integrated Variational Autoencoders for Robust and Interpretable
Generative Modeling [86.9726984929758]
We focus on the integration of incomplete physics models into deep generative models.
We propose a VAE architecture in which a part of the latent space is grounded by physics.
We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
arXiv Detail & Related papers (2021-02-25T20:28:52Z) - Data Assimilation in the Latent Space of a Neural Network [7.555120710924906]
Reduced Order Modelling technique is used to reduce the dimensionality of the problem.
We formulate a new methodology called Latent Assimilation that combines Data Assimilation and Machine Learning.
This methodology can be used for example to predict in real-time the load of virus, such as the SARS-COV-2 in the air by linking it to the concentration of CO2.
arXiv Detail & Related papers (2020-12-22T14:43:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.