Development and Evaluation of an Intelligent Control System for

Sustainable and Efficient Energy Management

BIBARS AMANGELDY *, NURDAULET TASMURZAYEV, YEDIL NURAKHOV,

SHONA SHINASSYLOV, SAMSON DAWIT BEKELE

Joldasbekov Institute of Mechanics and Engineering,

Building 2, Shevchenko Street, Medeusky District, Almaty,

KAZAKHSTAN

*Corresponding Author

Abstract: - This paper presents a comprehensive study on the integration of Intelligent Control Systems in the

improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30%

reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing

at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy

management, offering a scalable model for sustainable and efficient industrial practices. Future work will

explore broader applications and the incorporation of emerging technologies to further enhance the system's

capabilities and applicability in diverse industrial settings.

Key-Words: - Intelligent Control System, Machine Learning, Digital Twin, SCADA, Energy Efficiency,

Received: March 18, 2023. Revised: October 21, 2023. Accepted: December 2, 2023. Published: December 31, 2023.

1 Introduction

minimize environmental impact. Among the

technologies spearheading this transformation are

Supervisory Control and Data Acquisition

(SCADA), Machine Learning, and Digital Twin.

the real-time monitoring and control of industrial

processes. They have evolved with technological

advancements to collaborate with microprocessors

wirelessly, collecting measurements from distant

locations, thereby augmenting the efficiency and

integration with locally and regionally generated

technologies fabricates a robust framework for an

ICS directed towards sustainable and efficient

energy management. This paper elucidates the

architecture and evaluation of such a system,

underscoring its effectiveness in ameliorating

energy efficiency and sustainability in real-world

scenarios.

architecture of the proposed system and present the

The architecture of the proposed intelligent control

system is designed to facilitate sustainable and

efficient energy management by integrating

SCADA, ML, and Digital Twin technologies. The

detailed architecture of the system can be seen in

Figure A1 in Appendix. For a detailed view of the

communication and control architecture, which

highlights the synergy between various

communication protocols, machine learning models,

and external platform integrations, refer to Figure

2.1 Physical Layout

At the system’s physical core, there are:

absolute pressure (18), temperature (20), and

humidity sensors (21) form the primary line of

defense. By constantly monitoring ambient

conditions, these sensors ensure that operations are

kept within specified parameters. Their integration

guarantees a stable environment and prevents

potential hazards that could arise from system

Fig. 2: Design of the control box

The seamless communication between physical

systems and their digital twins is crucial for

ensuring real-time measurements and data accuracy,

[11]. By leveraging multiple protocols, the system

ensures that data flows without hindrance:

SCADA is invaluable. It aids in holistic process

for long-term analytics and report generation.

digital twin technology serves as a cornerstone for

achieving a seamless interaction between our system

and external platforms. Digital twins provide a

dynamic, real-time representation of the system,

aiding in both monitoring and control. The

utilization of smart digital twins supports decision-

making on the development of intelligent integrated

energy systems and subsequent management,

The data collected was analyzed both

The feedback from the operators indicated a

higher level of satisfaction due to the ease of

operation and the timely insights provided by

the system.

directly aligning to enhance energy efficiency

and operational sustainability. XGBoost's

gradient boosting is crucial for efficiently

processing large datasets to predict system

failures or maintenance needs, thus contributing

to reduced downtime and improved operational

efficiency. The Random Forest algorithm

complements this by effectively handling

classification and regression tasks, crucial for

operational demands, thus bolstering our

system's efficiency and sustainability.

4 Discussion

Our study's results reveal a significant leap in

energy management within industrial settings,

underscored by substantial gains in energy

efficiency, sustainability, and operational

performance. The integration of SCADA, ML,

and Digital Twin technologies, particularly the

industrial practices. The predictive maintenance

capabilities, powered by advanced ML

algorithms, significantly reduced downtime,

demonstrating the system's operational

superiority. Our findings contribute to the

evolving field of intelligent energy systems,

offering a novel approach that blends traditional

industrial control with cutting-edge digital

as advanced IoT applications and newer AI

paradigms, to enrich our system. These future

efforts are not just about system enhancement

but are pivotal in contributing to the evolving

narrative of sustainable and efficient energy

References:

[1] K. Sayed and H. A. Gabbar, “Chapter 18 -

SCADA and smart energy grid control

automation,” in Smart Energy Grid

Engineering, H. A. Gabbar, Ed., Academic

Press, 2017, pp. 481–514. doi:

10.1016/B978-0-12-805343-0.00018-8.

[2] J. Figueiredo and J. Sá da Costa, “A SCADA

system for energy management in intelligent

SCADA system in a real Microgrid for smart

building applications,” Renew. Energy, vol.

171, pp. 1115–1127, Jun. 2021, doi:

10.1016/j.renene.2021.03.008.

[4] A. V. Jha, B. Appasani, D. K. Gupta, S.

Ramavath, and M. S. Khan, “Machine

Learning and Deep Learning Approaches for

Energy Management in Smart Grid 3.0,” in

Smart Grid 3.0: Computational and

Communication Technologies, B. Appasani

managing the energy efficiency of the public

sector as an approach towards smart cities,”

Int. J. Inf. Manag., vol. 58, p. 102074, Jun.

2021, doi: 10.1016/j.ijinfomgt.2020.102074.

[6] W. Ahmed et al., “Machine Learning Based

Energy Management Model for Smart Grid

and Renewable Energy Districts,” IEEE

Access, vol. 8, pp. 185059–185078, 2020,

doi: 10.1109/ACCESS.2020.3029943.

[7] W. Yu, P. Patros, B. Young, E. Klinac, and

pp. 334–346. doi: 10.1007/978-3-031-43792-

10.1088/1757-899X/495/1/012067.

[16] J. Li, Y. Guo, X. Zhang, and Z. Fu, “Using

Hybrid Machine Learning Methods to

Predict and Improve the Energy

Consumption Efficiency in Oil and Gas

Fields,” Mob. Inf. Syst., vol. 2021, p.

e5729630, Dec. 2021, doi:

10.1155/2021/5729630.

[17] A. Eso and D. Omorogiuwa, “Real-Time

Effective Monitoring and Control in Oil and

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

This research was funded by a grant from the

Ministry of Education and Science of the Republic

of Kazakhstan BR18574136 "Development of deep

learning and intellectual analysis methods for