Solar radiation forecasting based on convolutional neural network and ensemble learning.

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 24 h. 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. • A combination of VMD and CNNs provide a novel methodology to forecast GHI. • GHI forecast aid the development of more accurate policies for energy management. • Methodologies to forecast GHI for short and long-term horizons are becoming crucial. [ABSTRACT FROM AUTHOR]