3 research outputs found
Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
Wind lidar observations are characterized by a Carrier-to-Noise-Ratio that is often used to filter the observations. The choice of the Carrier-to-Noise-Ratio threshold value for the wind lidar observations is found to have an effect on the climatological wind speed distribution in such a way that when the Carrier-to-Noise-Ratio (CNR) threshold value is increased the wind speed distribution is shifted to higher values. Based on one year of observations carried out with a wind lidar from 126 m to 626 m height at the FINO3 (Forschungsplattform in Nord- und Ostsee Nr. 3) research platform in the North Sea, the effect that the choice of the Carrier-to-Noise threshold value has on the climatology of the wind speed and direction as well as the wind power density in relation to wind energy is illustrated and discussed. In the one-year data set considered here it is found that for thresholds larger than −29 dB, the mean wind speed and wind rose measured by the wind lidar become a function of the threshold value, and for values smaller than ~ −29 dB further decrease of the Carrier-to-Noise-Ratio threshold has a minor effect on the estimated mean wind speed and wind rose. The analysis of the data set from the North Sea shows that the limit for the Carrier-to-Noise-Ratio of the observations should be −29 dB or less to obtain a threshold independent estimate of the mean wind speed and wind rose. Alternatively, all valid observations should be used for the analysis. Although this study is specific for the conditions in the North Sea, we suggest that for a representative estimation of the wind resource with wind lidars, the effect of the CNR threshold filtering on the wind distribution should be studied when the recovery rate is less than 100%
Assessment of Renewable Energy Resources with Remote Sensing
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii
Fernando Ramos Martins
Editorial for the Special Issue: Assessment of Renewable Energy Resources with
Remote Sensing
Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1
André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil
Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7
Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller
On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region
Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33
JoaquĂn Alonso-Montesinos
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera
Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43
Román MondragĂłn, JoaquĂn Alonso-Montesinos, David Riveros-Rosas, Mauro ValdĂ©s, HĂ©ctor EstĂ©vez, Adriana E. González-Cabrera and Wolfgang Stremme
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61
Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79
Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao
Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101
Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125
Ian R. Young, Ebru Kirezci and Agustinus Ribal
The Global Wind Resource Observed by Scatterometer
Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147
Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura
Coastal Wind Measurements Using a Single Scanning LiDAR
Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165
Cristina Sáez Blázquez, Pedro Carrasco GarcĂa, Ignacio MartĂn Nieto, MiguelAngel ´ MatĂ©-González, Arturo Farfán MartĂn and Diego González-Aguilera
Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods
Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189
Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz
A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data
Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma