Print Email Facebook Twitter Short-Term Irradiance Forecasting using All-Sky Images and Deep Learning Title Short-Term Irradiance Forecasting using All-Sky Images and Deep Learning Author Doodkorte, Pim (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Martinez Lopez, V.A. (mentor) Ziar, H. (mentor) Isabella, O. (mentor) Cremer, Jochen (mentor) Degree granting institution Delft University of Technology Programme Electrical Engineering | Sustainable Energy Technology Date 2021-06-18 Abstract Short-term solar forecasting is crucial for large scale implementation of solar energy and plays an important role in grid balancing, energy trading, and power plant operation. Cloud movement is the main source of unpredictability within solar forecasting and can be recorded using All-Sky Imagers. Conventional cloud modelling methods using image analysis techniques are unable to extract the spatial configuration and the temporal dynamics of clouds, resulting in poor predictions of the interaction with solar radiation. The goal of this study is to create a deep learning model for short-term irradiance forecasting between 0 and 21 minutes into the future using all sky images combined with auxiliary data. The model performance was assessed by comparing the deep learning model with the persistence model and showed that the deep learning model outperforms the persistence model with 24.8%. A sensitivity analysis to data usage is performed showing that besides using more data, also the variation of using multiple years of data results in better performance. Furthermore, the sensitivity of the model to input variables is assessed, showing that using the clear sky irradiance as input improves model performance with 16% and that meteorological data does not improve performance. Additionally, the model performance was evaluated during different sky conditions showing that the deep learning model outperforms the persistence model for all sky conditions, except overcast conditions. An example of the model behavior is extensively described, showing that the deep learning model tends to predict the trend of the irradiance fluctuations rather than the actual fluctuations. Next to that is in this study shown that the current deep learning model occasional miss important weather events, like obscuration of the Sun, resulting in large irradiance prediction errors. A pathway for future improvements for deep learning models to forecast the short-term irradiance is provided. Subject Deep LearningSky CameraIrradiance Forecasting To reference this document use: http://resolver.tudelft.nl/uuid:d5f3233a-6306-491d-984b-ca0a4e6596ee Embargo date 2023-06-18 Part of collection Student theses Document type master thesis Rights © 2021 Pim Doodkorte Files PDF MSc_thesis_PimDoodkorte_finalV7.pdf 3.03 MB Close viewer /islandora/object/uuid:d5f3233a-6306-491d-984b-ca0a4e6596ee/datastream/OBJ/view