A Review of Multi-source Data-driven PV Facilities in Agricultural Regions for Low-Carbon Operation and Decision-making

With the increasing global demand for low-carbon energy, the integration of photovoltaic (PV) facilities in agricultural regions has become crucial for sustainable and renewable energy solutions. This review paper aims to provide an overview of multi-source data-driven PV facilities in agricultural regions, focusing on their potential for low-carbon operation and intelligent decision-making. The review highlights the challenges associated with water scarcity in these regions and emphasizes the need for innovative approaches to optimize PV facility performance and enhance their integration with the existing energy infrastructure. It explores the utilization of various data sources, such as meteorological data, satellite imagery, IoT devices, and energy consumption patterns, to drive efficient operation and intelligent decision-making. Furthermore, the review examines the integration of advanced technologies like machine learning, data analytics, and optimization algorithms to leverage the potential of multi-source data in PV facilities. These technologies enable real-time monitoring, predictive analytics, fault detection, and optimal control strategies, contributing to improved performance, reliability, and sustainability of PV systems. The review concludes by discussing the challenges and future research directions in the field, emphasizing the importance of developing robust models, addressing data quality issues, and considering socio-economic factors in decision-making processes. Overall, the integration of multi-source data-driven approaches in PV facilities presents a significant opportunity for achieving low-carbon operation and enabling intelligent decision-making in agricultural regions, fostering sustainable and efficient PV installations in water-constrained areas.