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DC Field | Value | Language |
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dc.contributor.author | Masoom A. | |
dc.contributor.author | Kashyap Y. | |
dc.contributor.author | Bansal A. | |
dc.date.accessioned | 2021-05-05T09:23:31Z | - |
dc.date.available | 2021-05-05T09:23:31Z | - |
dc.date.issued | 2019 | |
dc.identifier.citation | Energy, Environment, and Sustainability , Vol. , , p. 45 - 71 | en_US |
dc.identifier.uri | 10.1007/978-981-13-3302-6_3 | |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/14610 | - |
dc.description.abstract | Since the availability of ground data is very sparse, satellite data provides an alternative method to estimate solar irradiation. Satellite data across various spectral bands may be employed to distinguish weather signatures, such as dust, aerosols, fog, and clouds. For a tropical country like India, which is potentially rich in solar energy resources, the study of these parameters is of crucial importance from the perspective of solar energy. Furthermore, a complete utilization of the solar energy depends on its proper integration with power grids. Because of its variable nature, incorporation of photovoltaic energy into electricity grids suffers technical challenges. Solar radiation is subjected to reflection, scattering and absorption by air molecules, clouds, and aerosols in the atmosphere. Clouds can block most of the direct radiation. Modern solar energy forecasting systems rely on real-time Earth observation from the satellite for detecting clouds and aerosols. © 2019, Springer Nature Singapore Pte Ltd. | en_US |
dc.title | Solar Radiation Assessment and Forecasting Using Satellite Data | en_US |
dc.type | Book Chapter | en_US |
Appears in Collections: | 3. Book Chapters |
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