Optimizing Grid-Connected Solar Energy Systems for Agricultural Applications in the Algerian Sahara (El Oued) Using AI-Based Maximum Consumption Point Tracking (MCPT)

This paper proposes an innovative AI-based method called Maximum Consumption Point Tracking (MCPT) to optimize solar energy systems connected to the electrical grid for agricultural use in the Algerian Sahara (El Oued), Algeria. By analyzing real-world consumption patterns of local farmers (averaging 20,000 kWh/year), the model dynamically allocates photovoltaic energy using a hybrid deep learning framework. The study demonstrates enhanced energy efficiency, minimized waste, and better load prediction under variable solar irradiance [1], [2].

• agricultural electricity
• load forecasting
• solar irradiance variability
• deep learning
• energy efficiency
• El Oued
• Algerian Sahara

• 7 — Énergie propre et d’un coût abordable
• 9 — Industrie, innovation et infrastructure
• 13 — Mesures relatives à la lutte contre les changements climatiques
• 2 — Faim « zéro »

• Photovoltaic System Optimization Techniques
• Smart Grid Energy Management
• Power Systems and Renewable Energy
• agricultural electricity
• load forecasting
• solar irradiance variability
• deep learning
• energy efficiency
• El Oued
• Algerian Sahara
• Renewable Energy, Sustainability and the Environment
• Electrical and Electronic Engineering
• Energy Engineering and Power Technology
• Energy
• Engineering