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Intelligent HVAC Systems for Smart Modern Building

July 2021
Periodicals of Engineering and Natural Sciences (PEN) 9(3):90-97

DOI:10.21533/pen.v9i3.2102

Authors:

University of Anbar

The modern smart building offers software solution and sensing the surrounding environment. However, this will be allowed management easily for leaders that are providing better control and optimize heating, ventilation, and air conditioning (HVAC) ،as well as this is consider from the important topics in mechanical engineering modern application. In this paper, a new intelligent HVAC system is proposed for modern smart building. The proposed system is heavily based on one of the most efficient tools of artificial intelligence which is a support vector machine. This technique will be depended on the data set to detect the HVAC system for any building. In this case, it will save time as well as it has the ability to provide a suitable system without any delay. The HVAC system that proposed in this paper is very important issues in design modern building. According to for training and testing phases for the proposed system, we can easily notice efficiency and effectiveness for the cooling and heating systems.

Performance metrics.

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ISSN 2303-4521

Periodicals of Engineering and Natural Sciences Original Research

Vol. 9, No. 3, July 2021, pp.90-97

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Intelligent HVAC systems for smart modern building

Harith M. Ali 1, Rashid M Aldaiyat 2

1 Mechanical Department, Engineering College, University of Anbar, Ramadi, Al-Anbar, Iraq

2Ministry of Public Works (MPW), Kuwait

ABSTRACT

The modern smart building offers software solution and sensing the surrounding environment. However,

this will be allowed management easily for leaders that are providing better control and optimize heating,

ventilation, and air conditioning (HVAC)

as well as this is consider from the important topics in mechanical

engineering modern application. In this paper, a new intelligent HVAC system is proposed for modern smart

building. The proposed system is heavily based on one of the most efficient tools of artificial intelligence

which is a support vector machine. This technique will be depended on the data set to detect the HVAC

system for any building. In this case, it will save time as well as it has the ability to provide a suitable system

without any delay. The HVAC system that proposed in this paper is very important issues in design modern

building. According to for training and testing phases for the proposed system, we can easily notice efficiency

and effectiveness for the cooling and heating systems.

Keywords:

HVAC building automation, Intelligent control of HAVC systems, HVAC Systems

of Smart Building, Building management system.

Corresponding Author:

Harith M. Ali

Mechanical Department, Engineering

University of Anbar

Anbar, Iraq

eng.harith85@uoanbar.edu.iq

1. Introduction

In general, a smart building is foreseeable to be modified with alteration of occupancy requirements and also

the advance of computer technology and information technology. In-office business systems, the rapid updating

of microcomputer technology has dislodged the untampered implementation of computer technology. The

liberalization and development of telecommunication technology have, like, the efficiency of office business

systems and fostered internationalization [1]. Smart buildings are considered one of the most important modern

trends in civil engineering. The persons are spent most of their time in a different building (indoor) and they

aim to provide support, safety and comfort for their activities at minimum requirements of the expenses. The

coinciding of computation and communication capabilities in various buildings and their subsystems is

providing by the profound integration of the internet and cyber-physical technologies within these buildings and

their subsystems. These technologies present tools to obtain the buildings elaborate and reality intelligent

control methods [2]. In recent years, the building automation system had acquired a great amount of importance.

Many studies results have been improved and the previous methods use centralized control in pneumatic

actuators, the modified version of building automation system based on control distributed with direct digital

equipment. More recently, these systems have depended on the artificial intelligence techniques and the

network, where a hierarchical database was often utilized to detect and monitor facility malfunctioning [3], [4].

Because of the rapid growth in the information system and network technology, the working and living ways

have been influenced directly and indirectly. The intelligent buildings have gradually prolonged from office

building automation to residential building automation because the traditional residential buildings became not

able to accommodate the impact that is made by the developing technology. The concept of smart houses in

communities will be the future trend of buildings. In this paper, a prediction system is proposed for modern

PEN Vol. 9, No. 3, July 2021, pp.90-97

build to establish smart heating and cooling systems. This system will help mechanical engineering design

HVAC for a modern building. However, the basic structure of this building is shown in Figure 1.

Figure 1. Basic Infrastructure of smart building [5]

This paper is organized as follow: related works present in section 2. The methodology of this system presents

in section 3. Finally, the Conclusions presents in section 4.

2. Related works

The feasibility study of HVAC is considered the main concern for engineers. For this, we are working to find

an optimal solution for modern building by establishing HVAC systems. This proposed system is heavily based

on machine learning. In [6], an automatic control system of air conditioner and intruder detection surveillance

system for smart buildings is proposed. The proposed system has the ability to control home temperature and

the detection of any unknown person entering the house. In order to improve the efficiency of energy for

buildings, a software system perspective is presented in [7]. The results show that the proposed model provides

fine-grained building control, maximizing its occupants’ comfort and reduces energy consumption. In [5], the

main motives and systems of smart buildings are identified and correlated by associating them with the

beneficiaries: users, owners, and the environment. The results show eleven motives and eight systems, and these

can be improved by more than one motive. The review of recent researches on the models of artificial

intelligence technologies in smart buildings out of the concept of demand response programs (DRPs) and

building management system (BMS) is presented in [8]. This paper presents a discussion about the open future

directions and challenges of research on AI application for smart buildings. The surrogate object-

communication model concept with three-layered network architecture is used [9] to develop an intelligent

energy management network (IEMN). The results show that the proposed system presents many services such

as the distributed intelligent management, analysis online and the ability to processing the data. In [1], a platform

to manage intelligent environments behavior is presented. There are several services presented in this work, in

particular, the proposed model has the ability to support the needs of the emerging workplace such as hoteling

and extemporized group settings representing by users' preferences about their work environments separated

from the actual configurations of the physical spaces, they occupied at a given time. Discussion about remote

control system over the Internet or the telephone for the new generation air conditioner is presented [10]. The

results show that the control of Internet used indoors and the phone control will be in the outdoors or on the car

moving. Web-based access and the integration of BMS and FMS are two issues discussed in [11]. The two

issues are addressed to integrate control networks by the Internet protocols, infrastructures access BMS remotely

via the Internet and to use Internet/Intranet for building management. In [12], by depending on hybrid

knowledge, a model for a building management system is presented. The proposed approach enriches

management system awareness and presents better visions to the state of the system. In [13], some intelligent

buildings technologies and the internet things are studied to design model for buildings intelligent based on the

internet of things. This paper presents analysing of the wireless routing protocol for the buildings intelligent

PEN Vol. 9, No. 3, July 2021, pp.90-97

based on the internet of things, wireless networking modes and wireless communication protocol. This

technique has opened up new challenges in the field of building. However, our system is distinguished from

others by utilising machine learning to predicate heating or cooling systems for a modern building. In addition,

this system plays an important role to convert these systems for building from traditional techniques to smart.

3. Methodology

3.1 System overview

The proposed intelligent detection method comprises the main phases, which are presented in Figure 2.

Figure 2. Overall architecture of proposed detection system

After the dataset collected by the sensor, features have involved some letters and

symbols must be transferred into numbers to make the performance of the detection system more effective. in

this research, SVM is trained with the dataset for generating the intelligent detection system. Moreover, the

testing phase is required to detect heating and cooling states.

3.2 Dataset description

Dataset used in this research described in [15] were portion of the data set used. In this system, two sensors

commercial MOX (TGS 3870-A04 and SB-500 12, presented by Figaro and FIS) were exposed to mixtures

dynamic of humid synthetic air with (15–70% RH) and CO (0–20 ppm) in a gas chamber. According to the

manufacturer recommendations, the heater voltage was modified with 0.2–0.9 V range. The sensor output was

created at 3.5 Hz and then modified to 100 sample points. Each sensor presents a high

dimensional multivariate output with dimension 100.

Sensor data set collection

Cooling

Heating

Training phase

Testing Phase

SVM

yes

Preprocessing

Accuracy

training <=

threshold

PEN Vol. 9, No. 3, July 2021, pp.90-97

3.3 Experimental results

In this paper, results are employed to measure the efficiency of the proposed detection system. In more detail,

the results often tests are presented to measure system efficiency.

• Test 1

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 197, C=186

Table 1. Performance metrics

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

383

Result

(2) Results in the field of (C) cooling read by

heating in the field of (Ytr)

• Test 2

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 194, C=185

Table 2. Performance metrics

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

379

Result

(4) results in the field of (C) cooling, read of

heating in the field of (Ytr)

• Test 3

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 198, C=185

Table 3. Performance metrics.

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

383

Result

Result: (All heating is read as heating and all

cooling is read as cooling)

• Test 4

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 197, C= 186

Table 4. Performance metrics

Total number of fields

384

PEN Vol. 9, No. 3, July 2021, pp.90-97

errRate

0.0026

conMat = (C +Ytr)

383

Result

(2) results in the field of (C) cooling, read of

heating in the field of (Ytr)

• Test 5

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 198, C=185

Table 5. Performance metrics

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

379

Result

All heating is read as heating and all cooling

is read as cooling

• Test 6

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 199, C=185

Table 6. Performance metrics

Total number of fields

384

errRate

conMat = (C +Ytr)

384

Result

1 result in the field of (C) heating, read by

Tareq in the field of (Ytr)

• Test 7

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 198, C=185

Table 7. Performance metrics.

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

383

Result

All heating is considered heating and all

cooling is considered cooling

• Test 8

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 198, C= 185

Table 8. Performance metrics.

PEN Vol. 9, No. 3, July 2021, pp.90-97

Total number of fields

384

errRate

0.0026

conMat = (C +Ytr)

383

Result

All heating is considered heating and all

cooling is considered cooling

• Test 9

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 199, C= 185

Table 9. Performance metrics.

Total number of fields

384

errRate

conMat = (C +Ytr)

384

Result

1 result in the field of (C) heating, read by

cooling in the field of (Ytr)

• Test 10

Room temperature (20 Celsius), more than (20 Celsius) gives cooling No. (1) less than or equal to (20 Celsius)

give heating No. (2).

Ytr= 198, C= 186

Table 10. Performance metrics.

Total number of fields

384

errRate

conMat = (C +Ytr)

384

Result

1 result in the field of (C) cooling read by

heating in the field of (Ytr)

• Summary

The results of ten tests are summarized in table 11 as presented below:

Table 11. Summary Results of All Tests.

TEST

errRate

conMat

C+Ytr

Data

Reading

Results

Field:C

Field:Ytr

TEST 1

0.0026

186

197

383

384

Result: (2 results in the field of (C)

cooling read by heating in the Ytr field

TEST 2

0.0026

185

194

379

384

Result: (4 results in the field of (C)

cooling, read of heating in the field of

(Ytr))

TEST 3

0.0026

185

198

383

384

Result: (All heating is read as heating

and all cooling is read as cooling)

TEST 4

0.0026

186

197

383

384

Result: (2 results in the field of (C)

cooling, read of heating in the field of

(Ytr))

PEN Vol. 9, No. 3, July 2021, pp.90-97

TEST 5

0.0026

185

198

383

384

Result: (All heating is read as heating

and all cooling is read as cooling)

TEST 6

185

199

384

Result: (1 result in the field of (C)

heating, read by cooling in the field of

(Ytr))

TEST 7

0.0026

185

198

383

384

Result: (All heating is read as heating

and all cooling is read as cooling)

TEST 8

0.0026

185

198

383

384

Result: (All heating is read as heating

and all cooling is read as cooling)

TEST 9

185

199

384

Result: (1 result in the field of (C)

heating, read by cooling in the field of

(Ytr))

TEST

186

198

384

Result: (1 result in the field of (C)

cooling read by heating in the field of

(Ytr))

4. Conclusion

Mechanical Engineering is a hot topic for modern applications. One of these areas is heating and cooling system

at design modern building. Therefore, engineering spends money and time to configure HVAC system with all

of these efforts still build suffer from a shortage of services. For this, we design a modern system that has the

ability to predicate heating and cooling system for any building. However, this will arrange this HVAC system

automatically without any human interaction. On the other side, machine learning makes the proposed more

efficient by sensing all surrounding environment. In this case, the building will provide by HVAC automatically.

According to the testing/ evaluating system of the proposed system, we can easily notice that intelligent HVAC

can adopt with modern building with acceptance results.

In future directions, this proposed system will be tested with other data set to confirm its efficacy. Hence, this

dataset will be employed with other artificial tools, such as deep learning, neural networks and k nearest

neighbor.

5. References

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Conf. Pervasive Comput. Commun. 2003. (PerCom 2003), 2003.

[2] and D. R. R. Minerva, A. Biru, “Towards a definition of the Internet of Things (IoT),” IEEE Internet

Initiat., no. 1, 2015.

[3] A. C. W. W. and A. T. P., “Building automation in the 21st century,” Proc. 4th Int. Conf. Adv. Power Syst.

Control. Oper. Manag., pp. 819–824, 1997.

[4] P. C. Y. S. M. Tsai, S. S. Wu, S. S. Sun, “Integrate home service network on intelligent intranet,” IEEE

Trans. Consum. Electron, vol. 46, pp. 499–504, 2000.

[5] M. M. Froufe, C. K. Chinelli, A. L. A. Guedes, A. N. Haddad, A. W. A. Hammad, and C. A. P. Soares,

“Smart buildings: Systems and drivers,” Buildings, vol. 10, no. 9, pp. 1–20, 2020.

[6] R. K. U. R. V. N. P. K. S. S. Radha, “IoT Based Intelligent Control System for Smart Building,” 2020 Int.

Conf. Innov. Intell. Informatics, Comput. Technol., 2020.

[7] H. Chen, P. Chou, S. Duri, H. Lei, and J. Reason, “The design and implementation of a smart building

control system,” Proc. - IEEE Int. Conf. E-bus. Eng. ICEBE 2009; IEEE Int. Work. - AiR 2009; SOAIC

2009; SOKMBI 2009; ASOC 2009, no. January, pp. 255–262, 2009.

[8] H. Farzaneh, L. Malehmirchegini, A. Bejan, T. Afolabi, A. Mulumba, and P. P. Daka, “Artificial

intelligence evolution in smart buildings for energy efficiency,” Appl. Sci., vol. 11, no. 2, pp. 1–26, 2021.

[9] H. Y. Huang, J. Y. Yen, S. L. Chen, and F. C. Ou, “Development of an intelligent energy management

network for building automation,” IEEE Trans. Autom. Sci. Eng., vol. 1, no. 1, pp. 14–25, 2004.

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[10] I. G. Park, “The remote control system for the next generation air conditioners,” IEEE Trans. Consum.

Electron., vol. 47, no. 1, pp. 168–178, 2001.

[11] S. Wang and J. Xie, “Integrating Building Management System and facilities management on the Internet,”

Autom. Constr., vol. 11, no. 6, pp. 707–715, 2002.

[12] I. Szilagyi and P. Wira, “An intelligent system for smart buildings using machine learning and semantic

technologies: A hybrid data-knowledge approach,” Proc. - 2018 IEEE Ind. Cyber-Physical Syst. ICPS

2018, pp. 20–25, 2018.

[13] L. Wan and R. Xu, “Study of intelligent building system based on the internet of things,” AIP Conf. Proc.,

vol. 1820, no. March, 2017.

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Sep 2000

IHSNII (Integrated Home Service Network on intelligent Intranet) is a Web-based home service system; it supports a well-organized Internet service infrastructure, and allows the traditional HSASS (Home Service Application Supporting System) to connect to the Internet through the INTEGRATOR of IHSNII. This would solve the problem of inconvenience caused by the diversity of user interfaces and operation platforms of HSASSs which are provided by different vendors. The INTEGRATOR is a PC-based data acquisition and transformation node. It supports TCP/IP, and is capable of getting data from HSASS, encoding the data into XML document and then transferring the XML document to the server of the monitoring and management center. With IHSNII, the business partners of IHSNII can furnish their customers with Web service without constructing their own backbone of network, and reduce the service cost substantially. For end user, IHSNII provides not only Web service as a general ISP (Internet service provider) does but also a well software service. By XML exchanging, IHSNII can integrate the HSASSs of those business partners who are willing to provide data sharing, and provide end users with a uniform GUI (graphic user interface) for monitoring/setup service target through Web. For those HSASS service companies who are unwilling to provide data of customers, IHSNII acts as a monitoring data provider; it will encode the monitoring data in XML and pass the XML data to their own HSASS service companies

Managing behavior of intelligent environments

Jan 2003

S Y P C D Pdf
Wong

PDF and S. Y. P. C. D. Wong, "Managing behavior of intelligent environments," Proc. First IEEE Int. Conf. Pervasive Comput. Commun. 2003. (PerCom 2003), 2003.

Towards a definition of the Internet of Things (IoT)

Jan 2015

D R R Minerva
A Biru

and D. R. R. Minerva, A. Biru, "Towards a definition of the Internet of Things (IoT)," IEEE Internet Initiat., no. 1, 2015.

Building automation in the 21st century

Jan 1997
819-824

A. C. W. W. and A. T. P., "Building automation in the 21st century," Proc. 4th Int. Conf. Adv. Power Syst. Control. Oper. Manag., pp. 819-824, 1997.

Intelligent HVAC Systems for Smart Modern Building

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