Advancements in IoT-Based Smart Home Energy Management and Control

Abstract

The increasing demand for energy efficiency and automation in modern house- holds has led to the evolution of Smart Home Internet of Things (IoT) systems for energy management and load control. These systems enable households to optimize energy usage through intelligent automation, real-time monitoring, and user-centric functionalities. This review highlights recent advancements in IoT- based smart home systems, focusing on key functionalities such as automatic power control, load balancing, user-defined device prioritization, timer scheduling, and energy consumption monitoring. The integration of big data analytics with IoT further enhances decision-making by providing actionable insights into energy consumption patterns. Additionally, this paper explores the compatibility of these systems with both smart and non-smart devices, their ability to issue alerts and notifications, and their user-friendly interfaces enabling remote control. This comprehensive analysis aims to guide future innovations and address challenges in developing sustainable and efficient energy management solutions for smart homes

Introduction

???? Overview

Smart Home Energy Management Systems (SHEMS) are emerging as a response to increasing energy demands, the need for efficiency, and the rise of connected devices. These systems use Internet of Things (IoT) technologies to monitor, analyze, and control household energy usage across various appliances like lighting, HVAC, kitchen appliances, and security systems, creating a sustainable, user-centric environment.

?? Core Functionalities

1. Real-time Monitoring: IoT sensors track energy consumption continuously.

2. Automation & Control: Devices are scheduled or triggered based on user preferences or patterns.

3. Remote Access: Users can control appliances via mobile apps or voice assistants.

4. Load Balancing: Energy use is distributed to prevent circuit overloads.

5. Big Data Integration: Large datasets from sensors are analyzed to:

   • Predict energy demand

   • Optimize device usage

   • Provide actionable insights

6. Alerts & Insights: Users receive notifications about unusual energy usage or device failures.

???? Technologies & Methodologies

• IoT Integration: Enables seamless communication among smart devices.

• Machine Learning & Predictive Modeling: Used to forecast energy needs based on user behavior and environmental factors.

• Neural Networks (e.g., RNNs): Predict energy use based on occupancy and ambient conditions.

• Elastic Energy Management: Adjusts dynamically to match renewable energy availability with demand.

• Hybrid Models: Combine rule-based and AI approaches for smarter control.

???? Limitations and Challenges

As illustrated in Figure 2, the main barriers to adoption include:

1. High Setup Costs

2. Interoperability Issues: Difficulty integrating various device brands and platforms.

3. Data Privacy & Security Concerns

4. Dependence on Stable Internet Connectivity

5. Scalability Problems

6. User Reluctance & Complexity

???? Problem Statement

Despite their benefits, SHEMS face adoption hurdles due to:

• Lack of standardization

• Complexity

• Security concerns

• Cost barriers

• Integration issues with diverse devices

???? Motivation

Growing interest in renewable energy and sustainability is driving attention to smart home technologies. The potential for energy cost savings, efficiency gains, and user convenience makes SHEMS an attractive but underutilized solution.

???? Objectives of the Review

1. Identify key barriers to adoption and integration of smart energy systems.

2. Evaluate the effectiveness of current technologies in monitoring and control.

3. Explore how IoT enhances energy efficiency.

4. Assess SHEMS impact on cost reduction and sustainability.

5. Investigate future trends and research directions.

6. Provide recommendations to researchers, developers, and policymakers.

???? Methodologies & Case Studies (Sections 2–4)

• Focus on IoT, big data, and renewable energy integration.

• Analyze task scheduling algorithms and real-time control frameworks.

• Compare energy management approaches across residential and industrial contexts.

• Examine adaptability for solar systems, smart grids, and dynamic load balancing.

???? Trends and Future Directions

• Growth in peer-to-peer energy sharing within microgrids.

• Use of digital trust mechanisms for secure energy trade.

• Expansion into smart grid integration and decentralized energy systems.

• Continued development of scalable, secure, and user-friendly systems.

Conclusion

Thesurveyemphasizesthecrucialroleofenergymonitoringandcontrolsystems in optimizing energy consumption, enhancing efficiency, and fostering sustainability. Advanced IoT technologies allow these systems to monitor in real-time, prioritize loads,andintegratewithrenewableenergysources.Someofthemostimportantinno- vations include smart scheduling, adaptive frameworks for energy management, and cost-effectiveretrofittingoptionsforexistinginfrastructure.Althoughsignificant,some challenges persist with these systems: scalability, integration complexity, offline func- tionality, and data security, which means the development is still not mature enough to tackle different application scenarios. Overall, the surveyed systems indicate the transformational potential of intelligent energy management in cost reduction, over- load avoidance, and empowerment of users. These systems are crucial for achieving the goals of global energy sustainability and to meet the rising demand for smarter, more user-friendly, and adaptive solutions. Futureworkinenergymonitoringandcontrolsystemsmayfocusonscalabil- ity, interoperability, and user-centric designs. One important application area is the designofscalableloadbalancingsystemsthatmanageenergydistributioneffectivelywhile still accommodating user-defined priorities. Such features would help in smooth interfacingfromindividualhomeimplementationstolarge-scalecommercial/industrial applications. Other factors include the integration of advanced scheduling algorithms and dynamic pricing models, which can help optimize energy usage, reduce costs, and enhanceoverallsystemefficiency.Securitymeasuresshouldalsobeimproved,suchthat the energy data is protected through robust encryption, secure communication proto- cols, and user authentication mechanisms. Affordability is another factor in increasing the adoption of these systems, especially in low-resource environments. More specif- ically, it should research cost-effective options like modular hardware designs and optimized software that would maintain the level of performance without sacrificing accessibility. Ultimately, development of more intuitive, more user-configurable user interfaces would let technical and non-technical users alike monitor and manage their energyconsumptioneffectively,anditwouldmakethesystemevenmoreuser-friendly for people to adopt. By addressing these challenges, future systems will be able to deliver more efficient, secure, and accessible energy management solutions.

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Copyright

Copyright © 2025 Lokesh B, Likhitha Meher Medisetty, Roshini Sri Kalyani Yallabandi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.