Reprint

New Challenges in Solar Radiation, Modeling and Remote Sensing

Edited by
June 2023
222 pages
  • ISBN978-3-0365-7870-5 (Hardback)
  • ISBN978-3-0365-7871-2 (PDF)

This book is a reprint of the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing that was published in

Engineering
Environmental & Earth Sciences
Summary

This reprint gathers several works focused on recent and novel research in solar radiation modeling and forecasting where remote sensing techniques and retrieval information is employed as a part of the methodology. The use of machine learning algorithms in solar irradiance modeling and solar power forecasting is included in some of the works here presented. This is a topic with high interest nowadays because of the impact in solar energy deployment and in atmospheric studies as well. The recent improved remote sensing information and available data and the advances in machine learning algorithms have a relevant presence in this reprint indicating the current ad near future path of the contributions in solar radiation modeling.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
solar resource mapping; direct normal irradiance; δ-Eddington approximation; biomass burning; solar energy; PV energy production; energy losses; financial losses; forest fires; aerosol and cloud impact; surface solar irradiance (SSI); GK2A/AMI; machine learning; daily average downward surface shortwave radiation; spatial downscaling; temporal extrapolation; Himawari-8; Sentinel-2; DEM; black ice; radiation flux; local weather; topography; sky view factor; shadow pattern; solar cadaster; solar potential in rooftops; digital surface model; geographic information system; radiation observation; meteorological observation; perceived temperature; SOWEIG model; road worker PT; smart grid; renewable energy sources; solar radiation forecasting; wavelet transform; complete ensemble empirical mode decomposition with adaptive noise; solar energy; solar irradiance nowcasting; machine learning models blending; all sky imagers (ASI); MSG satellite images; Visible All-Sky image; cloud cover; global horizontal irradiation; short-term forecast; machine learning; n/a