Machine Learning Applications in Energy Management Systems for Smart Buildings

Aug 2, 2026

Uploaded by Hazel

This paper reviews machine learning applications for energy management in smart buildings, focusing on building-integrated energy systems, 5G-enabled smart energy management, and microgrid energy management using machine learning algorithms. It explains how AI-initiated learning processes and digital twins improve adaptability under unpredictable operational changes, including climate-driven variability. It also examines how 5G technology supports high-quality services and efficient building functions, while supervised and unsupervised learning enable effective microgrid control and monitoring. Overall, the review highlights how these technologies enhance efficiency, responsiveness, and sustainability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

1 Introduction

2 Review and discussion

Machine Learning Applications in Energy Management Systems for Smart Buildings 1Rajesh Singh, 2Kuchkarbaev Rustam Utkurovich, 3Ahmed Alkhayyat, 4Dr. G. Saritha, 5R. Jayadurga, and 6Dr. K. B. Waghulde *Department of Electronics & Communication engineering Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, India. † PhD, Head of Department, Agency for Innovative Development of the Republic of Uzbekistan. E-‡College of technical engineering, The Islamic university, Najaf, Iraq. § Department of ECE,Sri sairam Institute of Technology, **MBA,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai-127, 6Professor,Dr. D. Y. Patil Institute of Technology, Pimpri, [kishor.waghulde@dypvp.edu.in](kishor.waghulde@dypvp.edu.in) Abstract.This paper reviews the work in the areas of machine learning applications for energy management in smart buildings, 5G technology's role in smart energy management, and the use of machine learning algorithms in microgrid energy management systems. The first area focuses on the adaptability of building-integrated energy systems to unpredictable changes through AI-initiated learning processes and digital twins. The second area explores the impact of 5G technology on smart buildings, particularly in Singapore, emphasizing its role in facilitating high-class services and efficient functionalities. The third area delves into the application of various machine learning algorithms, such as supervised and unsupervised learning, in managing and monitoring microgrids. These broad areas collectively offer a comprehensive understanding of how machine learning can revolutionize energy management systems in smart buildings, making them more efficient, adaptable, and sustainable. 1 Introduction The evolution of smart buildings has been a focal point of research in recent years, with the integration of machine learning and advanced technologies playing a pivotal role in enhancing energy management systems. Alanne et al. (2022) delve deep into the adaptability of building-integrated energy systems. They emphasize the challenges posed by unpredictable changes in operational environments due to climate change and its consequences. Their research underscores the rapid advancements in artificial intelligence Corresponding Authour: *[drrajeshsingh004@gmail.com](drrajeshsingh004@gmail.com) †[r.kuchkarbaev@gmail.com](r.kuchkarbaev@gmail.com) ‡[ahmedalkhayyat85@iunajaf.edu.iq](ahmedalkhayyat85@iunajaf.edu.iq) §[saritha.ganesan@gmail.com](saritha.ganesan@gmail.com) **[r.jayadurga_mba@psvpec.in](r.jayadurga_mba@psvpec.in) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). (AI) and machine learning (ML), which have endowed buildings with the capability to learn and adapt. This adaptability, as they suggest, is crucial in addressing specific machine learning applications throughout a building's life-cycle, particularly when integrating smart technologies at a system-wide level. Fahim Huseien et al. (2022) explore the transformative potential of 5G technology in their research. Their work highlights Singapore's pioneering role in adopting 5G technology across various sectors, including smart buildings. The authors discuss the international trends in 5G applications for smart buildings and provide insights into the research and development initiatives undertaken in 5G labs. Their work underscores the profound impact of 5G technology on building construction, operation, and management, emphasizing its role in facilitating high-class services and efficient functionalities. Bourhnane et al. (2020) delve into the intricate relationship between machine learning algorithms and microgrid energy management systems. Their research emphasizes the importance of processing vast amounts o

FAQs

What topics does the paper review regarding smart buildings energy management?

The paper reviews machine learning applications for smart buildings, the role of 5G in smart energy management, and machine learning algorithms used in microgrid energy management systems.

How do AI-initiated learning processes and digital twins improve building energy systems?

They help building-integrated energy systems adapt to unpredictable operational changes by enabling learning under evolving conditions.

Which machine learning approaches are discussed for microgrid management?

The paper discusses supervised and unsupervised learning algorithms to manage and monitor microgrids effectively.

Aug 2, 2026

Uploaded by Hazel