Independent Control of Temperature and Humidity in Air Conditioners by Using Fuzzy Sliding Mode Approach

Yang, Lijian; Wu, Zhengtian; Xie, Mingyang; Jiang, Baoping; Fu, Baochuan

doi:10.1155/2020/1976584

Cited by 7 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 1992, twenty years after the first environmental conference, a conference of heads of state entitled (Environment and Development) was held in Brazil and evaluated the twenty-year performance of countries and the World Organization with the environmental issue [15,16]. The globalization of environmental aspects (such as global warming, thinning of the ozone layer, reduction of forests) and the relationship between the economic, social and cultural aspects of development and the environment in the set brought up a new perspective on environmental issues, which is called sustainable development.…”

Section: A-developments Of Environmental Viewsmentioning

confidence: 99%

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

2024

nano-ntp

View full text Add to dashboard Cite

Non-intrusive indoor environment sensing for occupancy detection and estimation has attracted extensive research interest in the building domain over the past decade due to the increasing number of applications for improving building infrastructure. Occupancy detection and estimation can be integrated into building appliances to manage lighting applications, intrusion detection in secured building areas, and occupancy-driven ventilation which has the potential to improve the performance of the Heating Ventilation and air-conditioning (HVAC) system through the finegrained occupant count to enhance the trade-off between thermal comfort and energy consumption. The research strategies for occupancy detection and estimation have utilized different technologies (including camera, wearable, and indoor environmental variables sensing through direct sensing and machine learning), which experience challenges in terms of acquiring essential sensory data related to occupancy information and correctly modeling the occupancy data due to hardware deployment limitations and underlying cost. This study explores existing technologies and methods for occupancy detection and estimation with their underlying issues. It provides a comprehensive procedure for occupancy modeling methodology using different machine learning methods and analyzing their comparative results to assist in decision making for choosing an optimal technique for solving occupancy detection and estimation problem. The results recommend Random Forest as a candidate model with high performance achieving 73.6% to 99.7% for occupancy detection and overall, 99.3% for occupancy estimation.

show abstract

Section: A-developments Of Environmental Viewsmentioning

confidence: 99%

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

2024

nano-ntp

View full text Add to dashboard Cite

show abstract

“…The study adopted a similar occupancy prediction classification used in [3] to categorizes reviewed occupancy prediction approaches using various technologies as described in Table 1. The environmental variables sensing approach is proposed in [13,14,15,16,21,24,25,26,27,28,29,30,31,32,33] to predict room occupation by measuring the variation of indoor parameters. The sensor modality adopted in these studies can be extended in many indoor sensing applications, including multi-sensing technology to observe concentrations of volatile organic compounds in the air, wireless sensor network technology for monitoring indoor air quality, and smart climate technology for weather forecast.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The decline or even discontinuation of adaptation of these technologies is forecasted in future smart buildings due to privacy concerns [3,11,12]. Consequently, a non-intrusive technique using indoor environmental (IE) sensing was introduced in [13,8,14,15,16] to measure the level of indoor variables, including temperature, humidity, and light intensity, to deduce the occupancy status and number. Studies in [17,18] demonstrate how occupancy parameters can be used to finetune the energy consumption through the " energy use per person" to establish a stochastic parameter correlation between occupancy and energy usage.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

show abstract

“…If the temperature is too high, lowering the humidity causes static buildup, which can cause equipment harm. Cooling the data center thoroughly was one of the approaches previously used to avoid server overheating [14,15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Indoor Temperature and Humidity Monitoring System

Shamang

Chukwuma-Uchegbu

El-nafaty

2020

JSETM

View full text Add to dashboard Cite

Monitoring Temperature and Humidity for Server Room is an IoT-based device that provides data when controlling temperature and humidity in the server room. The prototype has a variety of sensors that monitor both temperature and humidity parameters that can be calculated. This machine can be used to keep track of a room's or location's temperature and humidity. The suggested system transfers data to the cloud on a daily basis, allowing users to track data from anywhere. The device is fitted with features that dynamically include updates to consumers via the Telegram program for direct control and regulation. A Raspberry Pi and an Arduino module double as the robot brain. The Raspberry Pi is wired to a temperature and humidity sensor (DHT11). Any time these values cross the threshold set for each message sent to the user through the Telegram API. The user will remotely set the room temperature by sending an order via the telegram application based on the notification. Using telegram software or web server applications, the proposed framework efficiently tracks and dynamically manages orders.

show abstract

Independent Control of Temperature and Humidity in Air Conditioners by Using Fuzzy Sliding Mode Approach

Cited by 7 publications

References 40 publications

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Indoor Temperature and Humidity Monitoring System

Contact Info

Product

Resources

About