Experimenting Forecasting Models for Solar Energy Harvesting Devices for Large Smart Cities Deployments

• Published in: 2019 IEEE Symposium on Computers and Communications (ISCC) pp. 1177 - 1182
• Main Authors: Caruso, Antonio, Chessa, Stefano, Escolar, Soledad, del Toro, Xavier, Kuzman, Melisa, Lopez, Juan C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2019
• Subjects: Batteries; dataset; Energy harvesting; forecasting algorithms; IoT; Predictive models; Sensors; solar energy harvesting; Solar panels; Task analysis; testbed
• DOI: 10.1109/ISCC47284.2019.8969684
• EISSN: 2642-7389

To make sustainable large IoT deployments in smart cities, a promising approach is to develop a new generation of solar energy harvesting IoT devices based on the concept of energy neutrality. Key to this concept are the models for the forecast of energy production, which provide input to the energy-neutral schedulers governing the activities of the IoT devices. The development of such models however need to be validated against real-world conditions. To this purpose we propose a testbed aimed at the collection of real-world dataset about the energy parameters of energy harvesting IoT devices, and, on the base of such a dataset, we perform a comparative assessment of state of the art and novel energy production forecast models.