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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Factors Affecting Pulmonary Ventilation

Factors Affecting Pulmonary Ventilation

Besides the pressure difference between the external environment and the lungs, the airflow rate and ease of pulmonary ventilation are also influenced by three other factors: surface tension of the fluid in the alveoli, compliance of the lungs, and airway resistance.
Alveolar Surface Tension
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Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
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Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:

Assessment of Ventilation II: Respiratory Depth and Rhythm

Assessment of Ventilation II: Respiratory Depth and Rhythm

Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
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Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural

Chaoqun Zhuang^1,2, Ruchi Choudhary^1,2, Anna Mavrogianni³

¹Data-centric Engineering, The Alan Turing Institute, London, United Kingdom.

Building and Environment

|October 17, 2022

Summary

This study introduces an advanced AI model for predicting building occupancy, improving ventilation efficiency and reducing infection risk. The new method offers more accurate forecasts than traditional approaches, enhancing indoor environmental quality.

Keywords:

Autoencoder Bayesian deep neural network COVID-19 Occupancy prediction Ventilation

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Area of Science:

Building Science
Artificial Intelligence
Environmental Health

Background:

Effective ventilation is crucial for healthy indoor environments and mitigating airborne disease transmission.
Demand-controlled ventilation (DCV) systems use occupancy data for energy efficiency, but accurate prediction is challenging due to stochastic occupant presence.
Existing methods struggle to account for uncertainties in occupancy prediction.

Purpose of the Study:

To develop a probabilistic occupancy prediction model using an autoencoder Bayesian Long Short-term Memory (LSTM) neural network.
To evaluate the model's performance in predicting occupant numbers in an educational building.
To utilize probabilistic occupant profiles for optimizing ventilation rates in energy conservation and infection prevention modes.

Main Methods:

An autoencoder Bayesian LSTM model was developed to predict occupancy, incorporating model misspecification, epistemic, and aleatoric uncertainties.
The model was trained and tested using real-world data from an educational building.
Probabilistic occupant profiles were generated and applied to estimate optimal ventilation rates for different scenarios.

Main Results:

The proposed autoencoder Bayesian LSTM model demonstrated superior performance compared to traditional LSTM models, achieving up to a 5.8% reduction in mean absolute percentage error for 1-hour ahead predictions.
The model successfully predicted occupant numbers across different spaces within the same building.
Risk-aware decision-making schemes were enabled for flexible ventilation control.

Conclusions:

The study provides a novel solution for accurate occupancy forecasting in buildings.
Probabilistic decision-making frameworks can significantly optimize building ventilation strategies for various operational goals.
The findings support enhanced indoor environmental quality, energy efficiency, and disease mitigation through intelligent ventilation control.