# Artificial Intelligence of Things for Solar Energy Monitoring and Control
### Survey Methodology  
To ensure a comprehensive and systematic review, we employed a structured methodology to select, analyze, and compare relevant studies on AIoT in solar energy.  
**Search Process:** We conducted a systematic search using Google Scholar to identify relevant articles, the majority of the results sourced from IEEE, as well as journals published by MDPI and Elsevier. The search focused on peer-reviewed journal articles, conference papers, and review studies related to AI, IoT, and their integration in solar energy management. Keywords included \"AIoT in solar energy\", \"IoT-based solar monitoring\", \"AI in photovoltaic systems\", and \"fault detection in PV systems\".  
**Inclusion Criteria:** Our selection criteria ensured that we included studies specifically addressing AIoT applications in solar energy monitoring, optimization, fault detection, and predictive maintenance. In addition, we incorporated papers that provided fundamental definitions of AI and IoT to establish a strong theoretical foundation for our survey. Although we primarily focused on recent research, we also included some older articles that remain highly cited and influential in shaping AI and IoT applications in solar energy. These older studies were necessary for understanding the historical evolution of AIoT technologies and their impact on current advancements.  
**Exclusion:** we excluded general AI or IoT research without a clear focus on solar energy, non-peer-reviewed articles, and outdated studies with limited relevance to modern AIoT applications.  
**Bibliometric Analysis:** Our survey is based on a thorough review of 199 research papers, of which 127 specifically focus on the application of AI and IoT in PV systems. The distribution of these 127 papers over the years is shown in Figure 1. The remaining studies cover various aspects, including foundational definitions. The primary focus was on studies published between 2015 and 2025, ensuring that we captured the most recent advancements and emerging trends in the field.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Element** & **Observation** \\ \hline Strengths of Existing & • • Provide focused insights on either AI or IoT applications in solar energy. \\ Reviews & • Cover specific topics such as energy forecasting, MPPT algorithms, and IoT-based monitoring systems. \\ • & • Offer foundational understanding of individual technologies. \\ \hline Weaknesses of Existing Reviews & • • Lack of integrated perspective on AIoT (AI + IoT synergy). \\ • & • Limited discussion on real-time systems such as automated cleaning, tracking, and scheduling. \\ • & • Absence of comprehensive taxonomies for categorizing \\  & • AIoT applications. \\ • & • Few reviews cover implementation challenges. \\ \hline Gaps Identified and Addressed in This & • • Emphasis on integrated AIoT solutions tailored to solar energy systems. \\ Survey & • Introduction of a unified framework and structured taxonomy. \\ • & Comprehensive classification of AIoT applications across \\  & • Comparative analysis of existing studies, highlighting methodological trends. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of strengths, weaknesses, and gaps in existing reviews.