Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors

Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework a...

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Main Authors: Hsu, Hsieh-Chih, Pan, Chen-Yu
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Atmospheric modeling
Data models
data-driven control
Energy consumption
open data
Temperature
Training
Ventilation
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creator Hsu, Hsieh-Chih
Pan, Chen-Yu
description Indoor air quality reduces pollutants through different ventilation methods. Using different ventilation strategies is the focus of most scholars with limited resources. Therefore, we use outdoor environmental factors to data-driven control mechanical ventilation facilities.This proposed framework also optimizes the deep learning model (LSTM) through clustering analysis, and through cross-validation, the accuracy of the model is 97.45%. At the same time, this model can reduce energy consumption by 53%.
doi_str_mv 10.1109/ICASI57738.2023.10179512
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subjects Analytical models
Atmospheric modeling
Data models
data-driven control
Energy consumption
open data
Temperature
Training
Ventilation
title Data-Driven Control of Mechanical Ventilation Using Open Data Environmental Factors
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