A novel method for Indoor Air Quality Control of Smart Homes using a Machine learning model

Majdi, Ali; Alrubaie, Ali Jawad; Al-Wardy, Alia Haider; Baili, Jamel; Panchal, Hitesh

doi:10.1016/j.advengsoft.2022.103253

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…Ali Majdi et al published a study focusing on air quality control in smart buildings. In this study, a neural network with radial basis functions was used to predict the indoor air quality of a commercial office center in Mashhad, Iran [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

Long,

Luo,

Zhang

et al. 2023

Sensors

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Indoor air pollution is an urgent issue, posing a significant threat to the health of indoor workers and residents. Individuals engaged in indoor occupations typically spend an average of around 21 h per day in enclosed spaces, while residents spend approximately 13 h indoors on average. Accurately predicting indoor air quality is crucial for the well-being of indoor workers and frequent home dwellers. Despite the development of numerous methods for indoor air quality prediction, the task remains challenging, especially under constraints of limited air quality data collection points. To address this issue, we propose a neural network capable of capturing time dependencies and correlations among data indicators, which integrates the informer model with a data-correlation feature extractor based on MLP. In the experiments of this study, we employ the Informer model to predict indoor air quality in an industrial park in Changsha, Hunan Province, China. The model utilizes indoor and outdoor temperature, humidity, and outdoor particulate matter (PM) values to forecast future indoor particle levels. Experimental results demonstrate the superiority of the Informer model over other methods for both long-term and short-term indoor air quality predictions. The model we propose holds significant implications for safeguarding personal health and well-being, as well as advancing indoor air quality management practices.

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Section: Related Workmentioning

confidence: 99%

Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

Long,

Luo,

Zhang

et al. 2023

Sensors

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“…An ANN model and a control algorithm were developed and tested using transient simulation (TRNSYS 16.1) and matrix laboratory (MATLAB version 14) software. For air quality prediction, Majdi et al [68] applied a novel method using a neural network of the radial base function, with the inputs being temperature, air humidity and CO 2 , while the output was volatile organic compounds (VOCs) in the air. The model was trained for 138 days and tested for 3 days using 1104 samples and 24 samples, respectively.…”

Section: For Thermal Comfort and Iaq Controlmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

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“…Several studies have focused on COVID-19, pneumonia detection, and analysis with various algorithms. Most healthcare applications are gaining popularity with highlighted features supported by Artificial intelligence (AI), Machine Learning, and Deep Learning [17] , [18] , [19] .…”

Section: Literature Reviewmentioning

confidence: 99%

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications

Kathamuthu¹,

Shanthi²,

et al. 2023

Advances in Engineering Software

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A novel method for Indoor Air Quality Control of Smart Homes using a Machine learning model

Cited by 14 publications

References 25 publications

Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications

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