SMART AQI MONITORING AND PREDICTION SYSTEM FOR AIR CONDITIONERS USING IOT AND DEEP LEARNING

Abstract

SMART AQI MONITORING AND PREDICTION SYSTEM FOR AIR CONDITIONERS USING IOT AND DEEP LEARNING ABSTRACT OF THE INVENTION: This work, Smart AQI Monitoring and Prediction System for Air Conditioners Using IoT and Deep Learning, is an intelligent air quality management solution designed to enhance indoor air environments. The system integrates multiple loT-enabled AQI sensors (401, 402) that continuously monitor key air quality parameters such as particulate matter (PM2.5, PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), humidity, and temperature. These sensors transmit real-time data to a central processing unit, which utilizes deep learning algorithms to analyze and predict future air quality trends. Based on these predictions, the system autonomously adjusts airflow, filtration, and ventilation settings in the air conditioning unit (400) to maintain optimal indoor air quality. Additionally, the system includes a display unit (403) and a mobile application for remote monitoring and control, allowing users to view real-time air quality data, receive alerts, and manually configure system operations. TOTAL NUMBER OF WORDS: 2308 TOTAL NUMBER OF WORDS IN ABSTRACT:141 APPLICANT SIGNATURE Dated this 18 of OCTOBER 2024 PRINCIPAL EASWARI ENGINEERING COLLEGE Autonomous) \ , BharaffiiSalai, RamaDUrarh," Chennai - 600 08&

Specification

FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
The Patents Rules, 2003
PROVISIONAL/COMPLETE SPECIFICATION
(See section 10 and rule 13)

1. TITLE OF THE INVENTION
Smart AQI Monitoring and Prediction System for Air Conditioners Using loT and Deep
Learning
2. APPLICANTS (S)
(a) NAME: Easwari Engineering College
(b) NATIONALITY: INDIAN
(c) ADDRESS: Easwari Engineering College, Bharathi Salai, Ramapuram, Chennai - 600089

3P.REAMBLE TO THE DESCRIPTION:
DDAV/1 QIAMa 1
" IX XZ T 113Ixzt"/11J COMPLETE
The following specification particularly describes the
invention and the manner in which it is to be performed.
4. DESCRIPTION (Description shall start from next page.)
044lov-2024/132528/202441083995/Form 2(Title Page)
ATTACHED
5. CLAIMS (not-appHeftble-for-provtsional-speeifiefttiom Claims should start with the preamble "1/we
claim" on separate page)
ATTACHED
6. DATE AND SIGNATURE (to be given at the end of last page of specification)
ATTACHED
7. ABSTRACT OF THE INVENTION (to be given along with complete specification on separate page)
ATTACHED
Note:
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* Name of the applicant should be given in full, family name in the beginning.
* Complete address of the applicant should be given stating the postal index no./code, state and country.
* Strike out the column which is/arc not applicable

DESCRIPTION:
[0001] The Air Conditioner's Intelligent AQI Monitoring and Forecasting System A comprehensive, intelligent solution called loT and Deep Learning integrates cutting-edge 5 deep learning algorithms with loT-enabled AQI sensors to improve indoor air quality management. This invention consists of a network of Internet of Things (loT)-based sensors that are strategically positioned inside air conditioning systems to monitor several aspects of air quality in real time, including temperature, humidity, carbon dioxide (CO2), Volatile Organic Compounds (VOCs), and particle matter (PM2.5, PM10). Data on air 10 quality is continuously gathered by the system and sent to a central processing unit for analysis. The system can forecast future AQI levels by using deep learning models, which are based on past trends and environmental factors.

The system's deep learning algorithms are trained on big datasets in order to spot trends and predict when air quality will worsen or improve. With the help of these forecasts, the air conditioner may automatically modify its operation to ensure the best possible indoor - air quality by regulating ventilation rates, turning on air purification systems, or reducing airflow. To increase prediction accuracy, the system can also be connected with outside environmental data sources like weather stations and pollution monitoring networks. One important aspect of the system is its capacity to advise users in advance of impending poor air quality conditions by means of a mobile application or smart home interface, along with proactive alerts and advice. Additionally, the system has energy-efficient . modes that optimize interior comfort and energy usage by modifying the air conditioner's performance based on expected and real-time air quality. Modern smart homes as well as workplaces can benefit greatly from the smart AQI monitoring & prediction system, which guarantees a healthier and more pleasant living environment while lowering the need for manual intervention.

PRIOR ART AND BACKGROUD:
[0002] In the field of air quality monitoring and control systems, various technologies have been developed to address real-time air quality management using sensors and connected devices. CN-117873237-A: an unmanned intelligent environmental control system for archive warehouses, which integrates environmental sensors (temperature, humidity, and air quality) with loT to monitor and control conditions remotely. The system's focus is on preserving stored items through air quality management.

[0003J CN-117892655-A: A building networking virtualization management system, which includes an air quality monitoring subsystem.. It integrates loT sensors and cloud-based control-, enabling real-time adjustments of HVAC systems for better indoor air quality.
[0004] CN-116105299-A: An energy optimization method for air conditioning systems, incorporating air quality data to improve energy efficiency. The system uses sensors to monitor CO2, PM2.5, and other pollutants and automatically adjusts the air conditioning operation to optimize performance based on air quality.

[0005] CN-117851900-A: An atmospheric environment control system that leverages loT and Al to manage air quality in industrial environments. It uses multiple sensors to measure pollutants and optimizes HVAC systems to maintain a healthy and safe working atmosphere.
[0006] CN-117663414-A: Smart home air purification system that uses air quality sensors to monitor pollutants and automatically adjust air conditioning and filtration systems. The system includes mobile app control for remote monitoring and user feedback on indoor air quality.
[0007] CN-117655789-A: Smart HVAC control system that focuses on real-time air quality monitoring and dynamic energy management. The system uses loT-enabled air quality sensors to detect pollutants and adjusts HVAC operations to optimize energy efficiency while maintaining healthy air quality levels indoors.
[0008] CN-116988732-A: Automated ventilation and air purification system for both residential and commercial buildings. It integrates sensors for air quality monitoring (CO2, VOCs,- etc.) and uses Al-driven control to adjust airflow, temperature, and filtration to improve indoor air quality while reducing energy consumption.
[0009] CN-117543876-A: Intelligent air conditioning system with integrated loT sensors that monitor indoor air quality in real-time. The system communicates with external databases to retrieve air pollution forecasts and adjusts operations based on both current and future AQI data.

[0010] CN-116578934-A: Modular air quality monitoring and control system that can be
easily integrated into existing HVAC units. The system is designed to detect a wide range of pollutants and automatically modulates air conditioning or ventilation systems to maintain a healthy indoor environment. It also includes remote monitoring capabilities via IoT.

[0011] CN-117112345-A: Smart air purification and ventilation system designed for hightraffic
environments, such as offices or public buildings. The system uses air quality sensors to continuously monitor pollutant levels and dynamically adjusts HVAC operations to ensure optimal air quality. The system can be controlled and monitored through a cloud-based platform, providing real-time insights to building managers.

OBJECTIVE:
[0012] to create an intelligent system that can predict management and monitor air quality in indoor areas in real time. Using Internet of Things (loT)-enabled AQl sensors, this system continually monitors air quality indicators, Volatile Organic Compounds (VOCs), Carbon Dioxide (CO2), and particle matter (PM2.5, PM10). The system is intended to monitor air quality trends in the future and forecast them by incorporating deep learning algorithms that leverage past data and external environmental factors. The major goal is to make it possible for air conditioning systems to automatically modify ventilation,
filtration, and circulation in response to anticipated changes in air quality. This would allow for the maintenance of ideal indoor air quality without requiring continuous human
involvement. By optimizing HVAC operations, this system seeks to improve indoor air quality and energy efficiency, ultimately leading to a healthier living and working environment while consuming less energy. In order to further enable users to properly monitor and manage air quality, the system also incorporates functionality for user notifications and management via mobile apps or smart home interfaces.

SUMMARY:
[0013] It is an elegant, clever technology made to track and control indoor air quality in real time. The system continuously monitors important air quality indicators like particle matter, humidity, carbon dioxide, and volatile organic compounds by integrating IoT- 5 enable'd sensors. The system makes predictions about future trends in air quality using deep learning algorithms by analysing past data and environmental factors. With the use of this predictive capabilities, the air conditioning system can proactively maintain the best possible indoor air quality by automatically adjusting its settings, including ventilation, filtration, and airflow. Significant advantages of the system include better energy 10 economy, better air quality control, and increased user control through mobile applications. All things considered, this solution is a proactive, Al-driven method of optimising HVAC system energy consumption and fostering healthier, cosier interior environments.

DETAILED TECHNICAL DESCRIPTION:
[0014] For humidity and temperature monitoring, the system uses digital hygrometers and thermometers, ensuring the comfort and safety of the indoor environment. These sensors communicate with the central processing unit through Wi-Fi-enabled microcontrollers (such as ESP8266 or ESP32), forming the loT backbone that collects and transmits air quality data in real-time.

[0015] The system's deep learning models are powered by embedded processors, such as Raspberry Pi or NVIDIA Jetson Nano, which allow for the processing of large datasets and training of predictive algorithms. These models are trained on historical air quality data and external environmental conditions, such as weather reports and pollution indexes, to anticipate future air quality fluctuations.

[0016] The air conditioning unit itself is equipped with variable-speed fans, smart filters,
and automated vents that adjust airflow and filtration levels based on sensor data and
deep learning predictions. The system also features a mobile application or smart home
interface for user control, where notifications about air quality status and system
recommendations are displayed.

BRIEF DESCRIPTION OF THE DRAWING:
[0017] Figure 1 describes 2D Block Diagram of the System Architecture. This diagram illustrates the overall system architecture, showing the integration of loT-enabled AQI sensors (PM2.5, PM10, VOC, CO2) humidity, temperature) connected to the central processing unit (e.g., Raspberry Pi or NVIDIA Jetson Nano). The air conditioning unit is also depicted, highlighting the interaction between sensors, processing unit, and actuators for airflow, filtration, and ventilation control. The diagram outlines the real-time data flow from sensors to the processing unit and to the air conditioning system.

[0018] Figure 2 describes 2D Flowchart of Data Processing and Deep Learning Prediction. This flowchart shows the sequence of data acquisition, processing, and prediction in the system. It includes steps such as sensor data collection, transmission via IoT, data storage, and analysis using deep learning models. The prediction of future AQI levels and corresponding actions (e.g., adjusting ventilation or activating filtration) are also detailed.

[0019] Figure 3 describes loT-Enabled AQI Sensors in Operation. This 3D AQI sensors within the indoor environment. These sensors communicate wirelessly with the processing unit to provide real-time air quality monitoring.

[0020] Figure 4 describes 3D Diagram of the Air Conditioning Unit with Integrated Smart Control. This diagram shows the internal components of the air conditioning system, highlighting the integration of smart fans, automated vents, and air filters. The interaction between the AQI data and the air conditioning system's dynamic control mechanisms is also illustrated, demonstrating how the system adjusts to maintain optimal air quality.

[0021] Figure 5 describes the User Interface on a Mobile Device. This user interface on a mobile phone, where real-time air quality data is displayed. The interface also provides notifications regarding predicted changes in AQI and offers recommendations for adjusting ventilation or filtration settings. The image highlights the system's capability for remote monitoring and control through a user-friendly app.

[0022]-Figure 6* shows 2D Schematic of the System's loT Network Communication. This diagram illustrates the network communication setup, showing the connection between the AQI sensors, processing unit, and the mobile application. The diagram outlines how data is transmitted wirelessly using Wi-Fi, with real-time data being processed and predictions being sent to both the air conditioning unit and the user's mobile device.

LIST OF REFERENCE NUMERALS
400 - Air Conditioning Unit
401-Temperature Sensor
402 - PM sensor
403 - Display Unit

CLAIM:
04-ISov-2024/132528/202441083995/Form 2(Title Page)
l/WE Claim,

1. Smart AQI monitoring system of Claim 1, the Smart Air Quality Index (AQI) monitoring system integrated into air conditioning units is designed to optimize indoor air quality by utilizing a combination of advanced loT-enabled sensors and
intelligent control systems. The system incorporates a plurality of AQI sensors (401, 402), each of which is configured to continuously monitor various air quality parameters. These parameters include particulate matter (PM2.5, PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), humidity, and temperature. By detecting these key environmental factors, the sensors provide real-time data to a
central processing unit. The processing unit is engineered to receive this sensor
data, analyze it instantly, and utilize deep learning algorithms to predict future AQI
levels based on historical and environmental trends. The real-time data analysis allows the system to proactively adjust indoor air quality, ensuring a comfortable and healthy environment. The control system integrated within the air conditioning unit (400) acts upon the insights derived from the processing unit, autonomously
adjusting crucial operational settings like airflow, filtration, and ventilation to maintain optimal air quality. Furthermore, the system features a communication interface, which facilitates connection to external devices, enabling remote monitoring and control of the air conditioning system via a display unit (403). Users can access this interface through a mobile application or smart home device, allowing them to monitor air quality, receive alerts, and manually override system operations if needed.

2. The smart AQI monitoring system of Claim 1, wherein the deep learning model is trained to predict future AQI trends based on historical data, environmental factors, and external pollution data sources, allowing for proactive adjustment of air conditioning parameters.

3. The smart AQI monitoring system of Claim 1, wherein the system includes an automated filtration mechanism, designed to activate or adjust air filtration based on detected AQI levels, ensuring optimal indoor air quality is maintained with minimal user intervention.

4. The smart AQI monitoring system of Claim 1, wherein the system further comprises a mobile application or smart home interface, allowing users to monitor real-time air quality data, receive alerts about predicted air quality changes, and manually override or configure system settings remotely via the display unit (403).

5. The smart AQI monitoring system of Claim 1, wherein the processing unit is configured with energy-saving protocols that optimize the operation of the air conditioning unit (400) based on predicted air quality changes, reducing overall
energy consumption while maintaining a healthy indoor environment.

6. The smart AQI monitoring system of Claim 1, wherein the system includes external environmental data integration, using external pollution and weather data sources to improve the accuracy of deep learning predictions and system adjustments,
particularly in areas prone to outdoor pollution events.

FIG 1

Sensor Data Collection
(AQ1, PM2.5, VOC, CO2)

Transmission via IoT

Data Storage and Preprocessing
(Raspberry Pi, Database)
CDeep Learning Prediction
(Future AQI Levels)

Action: Adjust Ventilation/Filtration

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