Abstract
The Internet of Things (IoT) paradigm applied to the agriculture field provides a huge amount of data allowing the employment of Artificial Intelligence for multiple tasks. In this work, solar radiation prediction is considered. To the aim, Multi-Layer Perceptron is adopted considering a complete real complex use case and real-time working conditions. More specifically the forecasting system is integrated considering three different time forecasting horizons and, given different sites, needs and data availability, multiple input features configurations have been considered. The described work allows companies to innovate and optimize their industrial business.
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This work is within the Eco-Loop project (no. 2AT8246) funded by the Puglia POR FESR-FSE 2014-2020. Fondo Europeo Sviluppo Regionale. Azione 1.6 - Avviso Pubblico “InnoNetwork”.
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Impedovo, D. et al. (2020). An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_41
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