Cost-Effectiveness and Efficiency of PV Pumping Systems Utilizing Artificial Intelligence Algorithms

Abstract

We conduct this research for thirty-six months from 2021 to 2023 in Souk-Ahras (Tiffech), Algeria, to evaluate the effectiveness and costs of a direct-coupled photovoltaic pumping system for agricultural irrigation, considering the region's potential for solar energy. This study presents an analytical method of dimensioning based on water quantity, sunlight data, pump group efficiency, and well and reservoir characteristics to provide insights for better economic installation management. We evaluated the viability of a solar photovoltaic- SPV, water pumping system using the software PVsyst. However, we employed artificial intelligence- AI, methods to optimize irrigation and conserve water resources, thereby enhancing energy efficiency. Four AI methods, Support Vector Machine- SVM, Random Forest- RF, Naive Bayes- NB, and k-Nearest Neighbors-  kNN, are utilized. Energy-efficient systems promote a reduction in overall costs, enabling users to maintain financial balance while meeting their pumping needs. According to AI findings, the k-NN model exhibits high accuracy at 0.960 precision. The cost-effectiveness comparison of the SPV system to a diesel generator demonstrated a significant decrease in the cost of pumped water, thereby making the SPV system more viable and profitable. This approach has demonstrated its effectiveness and robustness.