Optimal Electricity Consumption in the Agricultural Sector and Its Implications for Startups: An AI Enabled Photovoltaic Approach

Abstract

This paper examines optimal electricity consumption in the agricultural sector, starting from the realities of remote areas where grid connection is weak or absent. Such constraints disrupt irrigation operations, hinder product cooling and cold chain storage, complicate the preservation of veterinary medicines and vaccines, and ultimately increase operating costs and production risks. In this context, photovoltaic (PV) solar systems have emerged as a practical and promising alternative. However, field performance is neither stable nor automatically efficient, it is shaped by multiple technical and operational factors dust accumulation, high temperatures, partial shading, installation and configuration issues, undetected faults, and poor storage management leading to energy losses and a higher effective cost per kilowatt hour actually delivered.The study aims to highlight the role that startups can play in rationalizing electricity use and improving PV system performance on farms through Artificial Intelligence (AI) and Internet of Things (IoT) solutions. These include generation forecasting, agricultural load scheduling (especially water pumping), maximum power point tracking, solar tracking, intelligent fault detection, and predictive maintenance, in addition to technical advisory services and user training. Using a descriptive–analytical approach based on recent literature and an analysis of Algeria’s national regulatory framework for startups, the paper concludes that optimal energy use in agriculture cannot be achieved through hardware alone. Rather, it requires a smart service layer driven by startups within a supportive ecosystem that includes targeted financing, data governance, stronger university–farm linkages, and technical interoperability standards.