Geostationary Operational Environmental Satellite

Deep learning

Operating system

Machine learning

Physics

Geology

Engineering

Geodesy

Aerospace engineering

Meteorology

Artificial intelligence

Geostationary orbit

Granularity

Benchmark (surveying)

Ecology

Satellite

Geography

Computer science

Photovoltaic system

Remote sensing

Real-time computing

Biology

Engineering Applications of Artificial Intelligence

The prediction of solar radiation allows estimating photovoltaic systems’ power production in advance, guaranteeing a more reliable and stable energy supply. In this work, we present a novel approach for short-term solar radiation forecasting that leverages multi-channel images from the geostationary satellites of the Meteosat series, coupled with GHI values in clear-sky conditions. We propose two distinct deep learning models, a 3D-CNN and a ConvLSTM, to forecast solar radiation in terms of GHI values, up to 6-h ahead with a temporal granularity of 15 min, over a test study area, the city of Turin, Piedmont, Italy. The models have been validated with ground GHI measurements, and the results show that the ConvLSTM consistently outperforms the 3D-CNN for longer forecasting horizons, achieving a MAD of 27.18% and an nRMSE of 0.57 for 6-h ahead predictions. To motivate the use of satellite images, we compared the performance of our approach with a baseline Smart Persistence model and another benchmark model, which previously achieved state-of-the-art performance on the same data set by exploiting various kinds of meteorological inputs. The proposed models outperform the Smart Persistence for predictions farther than 15-min ahead, achieving a Forecast Skill of 0.56 for predictions 6-h ahead. Furthermore, the comparison shows that using raw satellite images overcomes the performance achievable by solely using meteorological variables, reducing the RMSD by more than 3% and the MAD by 1.37% for prediction horizons greater than 4-h ahead.

Solar radiation forecasting with deep learning techniques integrating geostationary satellite images