Within the scope of the AGROVOLTA-AI project, we focus on agrivoltaic systems to contribute to meeting the increasing global demand for food and energy through sustainable and nature-based solutions. The project aims to integrate agricultural production and photovoltaic (PV) energy generation within the same land area to ensure land-use efficiency, enhance resource productivity, and reduce environmental impacts. To this end, an AI-powered optimization platform will be developed by combining digital twin technologies, machine learning-based predictive models, and data from IoT sensor networks. This integrated decision-support framework will enable the optimization of both agricultural productivity and PV energy generation, while supporting adaptive, data-driven, and sustainable operational strategies. The project is open to collaborations that will contribute to efficiency, sustainability, and innovation in agrivoltaic applications.

I am a PhD researcher in Energy Systems Engineering focusing on agrivoltaic systems and exergy-based decision support approaches. Our research group brings together multidisciplinary expertise in Electrical and Electronics Engineering, Computer Science, Artificial Intelligence, Modelling and Simulation, Renewable Energy, Power Electronics, Computer Vision, Machine Learning, and Agricultural Sciences. We have already established an agrivoltaic experimental setup, and our ongoing work aims to enhance system efficiency through AI-driven optimisation and data-based decision support. Our objective is to contribute to sustainable, scalable and high-impact agrivoltaic solutions that support both agricultural productivity and clean energy generation.