Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints
<|reference_start|>Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints: We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The output irradiance values are transformed to focus on unknown short-term dynamics. Inspired by control theory, a noise input is used to reflect unmeasured variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen test data, the model achieves a mean absolute error of 74.34 $W/m^2$ compared to a baseline 134.35 $W/m^2$ using the persistence of cloudiness model.<|reference_end|>
@article{hammond2024short-term,
    title={Short-Term Solar Irradiance Forecasting Under Data Transmission
  Constraints},
    author={Joshua Edward Hammond (1), Ricardo A. Lara Orozco (2), Michael Baldea
  (1 and 3), Brian A. Korgel (1 and 4) ((1) McKetta Department of Chemical
  Engineering The University of Texas at Austin, (2) Hildebrand Department of
  Petroleum and Geosystems Engineering The University of Texas at Austin, (3)
  Institute for Computational Engineering and Sciences The University of Texas
  at Austin,(4) Energy Institute The University of Texas at Austin)},
    journal={arXiv preprint arXiv:2403.12873},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12873},
    primaryClass={cs.LG}
}