Short-Term Solar Irradiance Prediction with a Hybrid Ensemble Model Using EUMETSAT Satellite Images

Abstract

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

“…This method is particularly useful in converting satellite cloud index data to solar irradiance values, which is essential for solar radiation forecasting and energy applications. Case studies in which the Heliosat method has been used include short-term forecasting of solar radiation [11,16,17], solar energy assessment using remote sensing technologies [18,19], and the deriving of shortwave solar radiation from satellite images [11,20]. The advantages of the Heliosat method include its ability to derive cloud transmission values from satellite data, its adaptability to different satellite sensors, and its capability to provide estimates of solar irradiance based on cloud cover information, contributing to improved solar energy forecasting and resource assessment.…”

Validation of Ground Based Solar Irradiance Measurements against Meteosat-Msg and Himawari-8 Satellite Data

“…This method is particularly useful in converting satellite cloud index data to solar irradiance values, essential for solar radiation forecasting and energy applications. Case studies where the Heliosat method has been used include short-term forecasting of solar radiation [11,16,17], solar energy assessment using remote sensing technologies [18,19], and deriving shortwave solar radiation from satellite images [11,20]. The advantages of the Heliosat method include its ability to derive cloud transmission values from satellite data, its adaptability to different satellite sensors, and its capability to provide estimates of solar irradiance based on cloud cover information, contributing to improved solar energy forecasting and resource assessment.…”