Artificial Intelligence for Enhancing the Efficiency of PV panels in Agricultural Farms

Abstract

In agricultural farms, PV is used for providing clean energy for irrigation, greenhouse management, crop processing, cold storage, animal farming and other farm uses, which is increasingly gaining traction in the market. Weather conditions, dust buildup, shading, temperature changes, equipment malfunctions and fluctuations in energy demand, however, affect the efficiency of the PV panels. This research aims to explore the use of Artificial Intelligence to optimize the performance of PV panels for use in agricultural systems. The framework proposed in this paper employs the machine learning algorithms to process real-time and historical data such as solar irradiance, panel temperature, ambient temperature, humidity, power output, dust level, and the energy consumption of the farm. Artificial Intelligence models are used to forecast PV performance, fault detection and degradation, optimal panel orientations, and appropriate maintenance actions. The system can also be integrated with an intelligent energy management system to optimise the use of solar energy, battery storage, and agricultural equipment, helping to ensure energy is used efficiently. The performance of the model is assessed based on prediction accuracy, MAE, RMSE, and energy-efficiency improvement. The potential outcomes are AI can minimise energy losses, more accurate fault detection, boost solar energy generation and enable more resilient farm operations. By combining Artificial Intelligence with photovoltaic technology, the study helps pave the way for smart and sustainable agricultural systems that are energy-efficient.