Personal thermal comfort models using digital twins: Preference
prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
- URL: http://arxiv.org/abs/2111.00199v1
- Date: Sat, 30 Oct 2021 07:43:11 GMT
- Title: Personal thermal comfort models using digital twins: Preference
prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
- Authors: Mahmoud Abdelrahman, Adrian Chong, and Clayton Miller
- Abstract summary: This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting indoor environmental preferences.
The framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA)
The results of a test implementation show 14-28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 thermal preference. The spatial context of a building can provide
information to models about the windows, walls, heating and cooling sources,
air diffusers, and other factors that create micro-environments that influence
thermal comfort. Due to spatial heterogeneity, it is impractical to position
sensors at a high enough resolution to capture all conditions. This research
aims to build upon an existing vector-based spatial model, called Build2Vec,
for predicting spatial-temporal occupants' indoor environmental preferences.
Build2Vec utilizes the spatial data from the Building Information Model (BIM)
and indoor localization in a real-world setting. This framework uses
longitudinal intensive thermal comfort subjective feedback from smart
watch-based ecological momentary assessments (EMA). The aggregation of these
data is combined into a graph network structure (i.e., objects and relations)
and used as input for a classification model to predict occupant thermal
preference. The results of a test implementation show 14-28% accuracy
improvement over a set of baselines that use conventional thermal preference
prediction input variables.
Related papers
- Real-Time 2D Temperature Field Prediction in Metal Additive
Manufacturing Using Physics-Informed Neural Networks [1.9116784879310036]
Accurately predicting the temperature field in metal additive manufacturing processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability.
We introduce a physics-informed neural network framework specifically designed for temperature field prediction in metal AM.
We validate the proposed framework in two scenarios: full-field temperature prediction for a thin wall and 2D temperature field prediction for cylinder and cubic parts, demonstrating errors below 3% and 1%, respectively.
arXiv Detail & Related papers (2024-01-04T18:42:28Z) - ClimaX: A foundation model for weather and climate [51.208269971019504]
ClimaX is a deep learning model for weather and climate science.
It can be pre-trained with a self-supervised learning objective on climate datasets.
It can be fine-tuned to address a breadth of climate and weather tasks.
arXiv Detail & Related papers (2023-01-24T23:19:01Z) - Environmental Sensor Placement with Convolutional Gaussian Neural
Processes [65.13973319334625]
It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica.
Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty.
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
arXiv Detail & Related papers (2022-11-18T17:25:14Z) - Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z) - LOPR: Latent Occupancy PRediction using Generative Models [49.15687400958916]
LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation.
We propose a framework that decouples occupancy prediction into: representation learning and prediction within the learned latent space.
arXiv Detail & Related papers (2022-10-03T22:04:00Z) - Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments.
Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion.
We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
arXiv Detail & Related papers (2022-06-29T07:49:48Z) - Information Entropy Initialized Concrete Autoencoder for Optimal Sensor
Placement and Reconstruction of Geophysical Fields [58.720142291102135]
We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements.
We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands.
We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
arXiv Detail & Related papers (2022-06-28T12:43:38Z) - Targeting occupant feedback using digital twins: Adaptive
spatial-temporal thermal preference sampling to optimize personal comfort
models [0.0]
This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with less data.
The proposed Build2Vec method is 18-23% more in the overall sampling quality than the spaces-based and the square-grid-based sampling methods.
arXiv Detail & Related papers (2022-02-22T07:38:23Z) - Personal Comfort Estimation in Partial Observable Environment using
Reinforcement Learning [8.422257363944295]
Most smart homes learn a uniform model to represent the thermal preference of user.
Having different thermal sensation for each user poses a challenge for the smart homes to learn a personalized preference for each occupant.
A smart home with single optimal policy may fail to provide comfort when a new user with different preference is integrated in the home.
arXiv Detail & Related papers (2021-12-02T04:01:44Z) - Mobility Map Inference from Thermal Modeling of a Building [1.5522829321999745]
We consider the problem of inferring the mobility map, which is the distribution of the building occupants at each, from the temperatures of the rooms.
Our proposed algorithm tackles down the aforementioned challenges leveraging a parameter learner, the modified Least Square Estimator.
Our work can be used in a wide range of applications, for example, ensuring the physical security of office buildings.
arXiv Detail & Related papers (2020-11-14T19:19:03Z) - Simulation and Optimisation of Air Conditioning Systems using Machine
Learning [4.511561231517167]
In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy.
This paper explores how to optimise the setpoints used in a particular room during its unoccupied periods using machine learning approaches.
arXiv Detail & Related papers (2020-06-27T06:42:25Z)
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.