Journal article
Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks
Smart and sustainable built environment
14 May 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
PurposeExposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.Design/methodology/approachThis research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building's ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.FindingsThe findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.Originality/valueHumans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being's and regular sensors' capacities.
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Details
- Title
- Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks
- Creators
- Panagiotis Karaiskos - Univ Texas San Antonio, Sch Architecture & Planning, San Antonio, TX USAYuvaraj Munian - Texas A&M University – San AntonioAntonio Martinez-Molina - Drexel UniversityMiltiadis Alamaniotis - Univ Texas San Antonio, Coll Engn & Integrated Design, Dept Elect & Comp Engn, San Antonio, TX USA
- Publication Details
- Smart and sustainable built environment
- Publisher
- Emerald Group Publishing
- Number of pages
- 23
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Architecture, Design, and Urbanism
- Web of Science ID
- WOS:001220208300001
- Scopus ID
- 2-s2.0-85192830616
- Other Identifier
- 991021890010304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Construction & Building Technology
- Engineering, Civil
- Green & Sustainable Science & Technology