AI-Driven Integrated Solar-Agrivoltaics Systems Transforming Food Security in West Africa.

ABSTRACT

This study evaluates an AI-Driven integrated solar-agrivoltaic circular economy model for West Africa with objectives to (1) quantify seasonal PV supply and energy demand for biomass valorization and cold storage, (2) measure conversion efficiencies of cassava peels, maize husks, and market vegetable waste into compost and animal feed, and (3) assess agrivoltaic impacts on crop yield and system-level environmental and economic performance. Methods combined year-long field trials at three sites, high-resolution solar and process monitoring, LCA, and techno-economic analysis with Monte Carlo uncertainty propagation. Key results: mean daily PV generation declined 44% from dry (6.1 kWh·kW−1·day−1) to rainy season (3.4 kWh·kW−1·day−1); post-harvest losses fell from 38.7% to 14.9% with solar cold storage (−23.8 percentage points); agrivoltaic shading increased tomato yield by 14% and leafy-green yield by 22%. LCA showed median GWP savings of 1,220 kg CO2-eq·t−1 (IQR 980–1,450); TEA base-case payback was 6.1 years. Uncertainty analysis indicates PV capacity factor variability can alter GWP savings by up to 28%.

Keywords: AI-Driven Solar agrivoltaics; Post-harvest loss reduction; Biomass valorization; Solar cold storage; Life cycle assessment; Techno-economic analysis.