Artificial intelligence, IoT, and solar PV-integrated home energy management systems: A review

• Critical review of AI and IoT-enabled home energy management systems (HEMS). • Focus on solar PV integration for sustainable smart homes and smart cities. • Comparative analysis of AI algorithms for forecasting, scheduling, and MPPT control. • Identifies key challenges: interoperability, cybersecurity, scalability, and cost. • Outlines future prospects including P2P trading, digital twins, and net-zero homes. The rapid growth of solar photovoltaic (PV) systems, residential energy storage systems (ESS), Artificial Intelligence (AI) and Internet of Things (IoT)-enabled sensing devices has increased the demand for intelligent Home Energy Management Systems (HEMS) capable of optimizing energy use in smart buildings. This review presents a structured synthesis of recent research on AI and IoT-integrated HEMS, focusing on forecasting methods, optimization strategies, appliance scheduling, and demand side management. The analysis reveals that advanced neural network variants and hybrid AI approaches can achieve high accuracy in forecasting; however, the practical deployment is often constrained by computational complexity, limited generalization, and low technology readiness. Reinforcement learning (RL) shows strong potential for adaptive real-time control; however, sample inefficiency and the simulation-to-reality gap remain major challenges. Across the reviewed literature, fragmented IoT communication standards, cybersecurity vulnerabilities, limited prosumer participation, and insufficient validation through hardware-in-the-loop and field testing emerge as key barriers to large-scale adoption. Based on these findings, the paper outlines a future research roadmap emphasizing hybrid AI-optimization frameworks, edge-computing architectures, blockchain-enabled peer-to-peer (P2P) energy trading, and standardized validation protocols. The review provides actionable insights to support the development of scalable, secure, and interoperable HEMS, contributing to the realization of smart, sustainable, and net-zero residential energy systems.

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Publication Details

Journal
Results in Engineering
Published
2026-01-02
DOI
https://doi.org/10.1016/j.rineng.2025.108954
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
21.93
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article

Artificial intelligence, IoT, and solar PV-integrated home energy management systems: A review

Kazi Sajedur Rahman, Nowshad Amin, Mahmuda Khatun Mishu, Boon Kar Yap et al.
1 citations
Results in Engineering
Smart Grid Energy Management
21.93
article

Artificial intelligence, IoT, and solar PV-integrated home energy management systems: A review

Kazi Sajedur Rahman, Nowshad Amin, Mahmuda Khatun Mishu, Boon Kar Yap, Mohammad Nur-E-Alam, Md. Rokonuzzaman, Asif Islam, Jagadeesh Pasupuleti
article en
1 citations

Abstract

• Critical review of AI and IoT-enabled home energy management systems (HEMS). • Focus on solar PV integration for sustainable smart homes and smart cities. • Comparative analysis of AI algorithms for forecasting, scheduling, and MPPT control. • Identifies key challenges: interoperability, cybersecurity, scalability, and cost. • Outlines future prospects including P2P trading, digital twins, and net-zero homes. The rapid growth of solar photovoltaic (PV) systems, residential energy storage systems (ESS), Artificial Intelligence (AI) and Internet of Things (IoT)-enabled sensing devices has increased the demand for intelligent Home Energy Management Systems (HEMS) capable of optimizing energy use in smart buildings. This review presents a structured synthesis of recent research on AI and IoT-integrated HEMS, focusing on forecasting methods, optimization strategies, appliance scheduling, and demand side management. The analysis reveals that advanced neural network variants and hybrid AI approaches can achieve high accuracy in forecasting; however, the practical deployment is often constrained by computational complexity, limited generalization, and low technology readiness. Reinforcement learning (RL) shows strong potential for adaptive real-time control; however, sample inefficiency and the simulation-to-reality gap remain major challenges. Across the reviewed literature, fragmented IoT communication standards, cybersecurity vulnerabilities, limited prosumer participation, and insufficient validation through hardware-in-the-loop and field testing emerge as key barriers to large-scale adoption. Based on these findings, the paper outlines a future research roadmap emphasizing hybrid AI-optimization frameworks, edge-computing architectures, blockchain-enabled peer-to-peer (P2P) energy trading, and standardized validation protocols. The review provides actionable insights to support the development of scalable, secure, and interoperable HEMS, contributing to the realization of smart, sustainable, and net-zero residential energy systems.

Results in EngineeringVol. 29
King Fahd University of Petroleum and Minerals (SA), Islamic University of Madinah (SA), Chitkara University (IN), Universiti Tenaga Nasional (MY), National University of Malaysia (MY)
Openalex Percentile: Top 3%
Smart Grid Energy Management
21.93
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