Artificial Intelligence of Things for Solar Energy Monitoring and Control
Source abstract
Abstract
In the rapidly evolving field of renewable energy, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) has become a transformative strategy for improving solar energy monitoring and control. This paper provides a comprehensive survey of Artificial Intelligence of Things (AIoT) applications in solar energy, illustrating how IoT technologies enable real-time monitoring, system optimization through techniques such as Maximum Power Point Tracking (MPPT), solar tracking, and automated cleaning. Simultaneously, AI boosts these capabilities through energy forecasting, optimization, predictive maintenance, and fault detection, significantly enhancing system performance and reliability. This review highlights key advancements, challenges, and practical applications of AIoT in the solar energy sector, emphasizing its role in advancing energy efficiency and sustainability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Solar Radiation and Photovoltaics · Photovoltaic System Optimization Techniques · Smart Grid Energy Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria
Autonomous University of the State of Quintana Roo, Chetumal 77019, Mexico
Funding
Autonomous University of the State of Quintana Roo (UQROO)
References (100+)
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