Artificial Intelligence-Based Optimal Energy and Water Management System in Agrivoltaics

Abstract

Agrivoltaic systems can improve renewable energy generation, water management, and agricultural productivity. This study proposes an artificial intelligence-assisted energy and water management framework integrating photovoltaic (PV) generation, battery energy storage systems, groundwater-fed irrigation, water storage, bidirectional grid interaction, and electric vehicle charging. The framework consists of two stages: day-ahead PV forecasting using a Bayesian optimization-based long short-term memory model, followed by mixed-integer linear programming for daily operating cost minimization under electrical, hydraulic, battery, soil-moisture, water-storage, and grid constraints. Agrivoltaic microclimate-related influences on evapotranspiration and precipitation transmission are represented in the soil moisture dynamics through literature-based coefficients. Six forecasting model families are evaluated in 21 configurations over 316 daily forecast origins. The selected model achieves a mean absolute error of 0.268 MW, equal to 4.37% of plant capacity, a weighted mean absolute percentage error of 14.5%, and R2 = 0.915, reducing persistence baseline error by 42.0%. Applied to a five-decare tomato-based system in Antalya, Türkiye, under eight operating scenarios, the framework achieves a minimum daily operating cost of EUR −31.321. It also maintains soil-water and storage tank levels within prescribed limits and provides up to 300 kW continuous grid support under emergency conditions while satisfying local agricultural and electrical demands.