International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
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Smart Solar Energy Systems: A Review of Intelligent Monitoring and Optimization Technologies Dhananjay Sharma1, Asst. Prof. Rajshri Pote2, Vivek Sotra3, Kapil Prajapati4, Asmit Hade5, Yash Chavhan6 2Assistant Professor, CSE, Priyadarshini College of Engineering Nagpur, Maharashtra, India 13456UG Student, CSE, Priyadarshini College of Engineering Nagpur, Maharashtra, India
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Abstract - In the global shift to renewable energy, solar
solar power—especially through photovoltaic modules— has emerged as one of the most practical long-term solutions. While the global installed solar capacity continues to exceed terawatt levels, a significant gap persists between the theoretical efficiency of PV modules and their actual field performance.
photovoltaic (PV) technology has emerged as a key component. Despite its widespread adoption, conventional PV systems still struggle to maintain optimal output due to environmental stressors, inefficient maintenance, temperature rise, and limited operational intelligence. By combining Internet of Things (IoT) monitoring, machine learning (ML), artificial intelligence (AI), automated cleaning, thermal management, and cutting-edge solar materials like lead-free perovskites, the new idea of Solar IQ-Smart systems introduces a paradigm shift and builds a more intelligent and adaptable solar ecosystem. Research from recent years demonstrates that intelligent solar systems can enhance overall performance by approximately 20–40%, restore 25–40% of efficiency losses caused by dust, reduce maintenance costs by up to 30%, and achieve highly accurate generation forecasting exceeding 95%. Although laboratory conversion is still lower, lead-free perovskite materials exhibit simulated efficiencies that are close to 29%. The technological developments, performance enhancements, regional adaptation requirements, and economic models detailed in earlier scholarly investigations are summarized in this review of the literature. Additionally, it presents a critical reflection on current technological gaps while identifying future opportunities for optimization and large-scale deployment. Together, the results highlight the potential of Solar IQ-Smart systems to convert solar PV from a static power-producing structure into an intelligent, selfoptimizing energy infrastructure appropriate for utilityscale, commercial, and residential installations.
Due to operational and environmental issues, solar PV panels suffer significant performance degradation. Research indicates that semiconductor efficiency is reduced by 0.38–0.5% per degree Celsius above standard testing conditions. These restrictions call for the creation of more sophisticated, automated, and user-friendly charging systems. A viable substitute is provided by wireless power transfer (WPT) technology, especially when it comes to inductive coupling. Wireless EV charging solutions improve safety, convenience of use, and eliminate the need for physical connectors by enabling contactless energy transmission. These systems can provide smooth and continuous charging experiences when integrated into residential garages, parking lots, or roads. Despite its benefits, wireless charging has drawbacks such alignment sensitivity, inefficient energy transmission, and a lack of intelligent control. The Internet of Things' (IoT) integration is crucial in this situation. Wireless EV charging is not only possible but also very effective and intelligent because to IoT's real-time monitoring, remote control, user identification, predictive maintenance, and smart energy management capabilities. During highirradiation hours, solar panels in hot regions may reach surface temperatures of 70–75°C, which can cause performance losses of 20–30% over the course of the operating period. In addition to thermal stress, dust accumulation on PV surfaces can obstruct solar radiation and cause energy losses in arid climates that range from 30% to 50% in just one month. Inconsistent maintenance schedules result from the labor and water consumption issues associated with manual cleaning. Likewise, the traditional monitoring of solar infrastructure through basic SCADA systems cannot ensure predictive diagnostics or autonomous optimization. In response to such limits,
Key Words: Smart solar systems, Photovoltaic optimization, Internet of Things (IoT), Artificial intelligence and machine learning, Automated solar panel cleaning, Thermal management, Perovskite solar cells.
1. INTRODUCTION Due to growing energy demand, growing concerns about climate change, and the depletion of non-renewable resources, the global energy landscape has continuously shifted toward environmentally sustainable power sources. Because of its scalability, small operating footprint, and suitability for a variety of climatic zones,
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