Reprint
Assessment of Renewable Energy Resources with Remote Sensing
Edited by
March 2021
244 pages
- ISBN978-3-0365-0480-3 (Hardback)
- ISBN978-3-0365-0481-0 (PDF)
This book is a reprint of the Special Issue Assessment of Renewable Energy Resources with Remote Sensing that was published in
Engineering
Environmental & Earth Sciences
Summary
The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
metaheuristic; parameter extraction; solar photovoltaic; whale optimization algorithm; cloud detection; digitized image processing; artificial neural networks; solar irradiance estimation; solar irradiance forecasting; solar energy; sky camera; remote sensing; CSP plants; coastal wind measurements; scanning LiDAR; plan position indicator; velocity volume processing; Hazaki Oceanographical Research Station; cloud detection; cloud coverage; sky camera; image processing; remote sensing; CSP plants; solar energy; solar irradiance forecasting; total sky imagery; geothermal energy; geophysical prospecting; time domain electromagnetic method; electrical resistivity tomography; potential well field location; GES-CAL software; smart island; solar energy; solar radiation forecasting; light gradient boosting machine; multistep-ahead prediction; feature importance; voxel-design approach; shading envelopes; point cloud data; computational design method; passive design strategy; lake breeze influence; hydropower reservoir; solar irradiance enhancement; solar energy resource; wind speed; extreme value analysis; scatterometer; feature engineering; forecasting; graphical user interface software; machine learning; photovoltaic power plant; surface solar radiation; global radiation; solar energy; satellite; Baltic area; coastline; cloud; convection; climate; renewable energy resource assessment and forecasting; remote sensing data acquisition; data processing; statistical analysis; machine learning techniques