Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy.

Solar radiation forecasting based on convolutional neural network and ensemble learning / Cannizzaro, Davide; Aliberti, Alessandro; Bottaccioli, Lorenzo; Macii, Enrico; Acquaviva, Andrea; Patti, Edoardo. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 181:(2021). [10.1016/j.eswa.2021.115167]

Solar radiation forecasting based on convolutional neural network and ensemble learning

Davide Cannizzaro;Alessandro Aliberti;Lorenzo Bottaccioli;Enrico Macii;Edoardo Patti
2021

Abstract

Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2899292