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A Shadow Histogram Algorithm to Dete...
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ProQuest Information and Learning Co.
A Shadow Histogram Algorithm to Determine Clear Sky Indices for Sky Imager Short Term Advective Solar forecasting.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
A Shadow Histogram Algorithm to Determine Clear Sky Indices for Sky Imager Short Term Advective Solar forecasting./
作者:
Sheth, Nishank Mihir.
面頁冊數:
1 online resource (34 pages)
附註:
Source: Masters Abstracts International, Volume: 56-03.
標題:
Environmental science. -
電子資源:
click for full text (PQDT)
ISBN:
9781369697377
A Shadow Histogram Algorithm to Determine Clear Sky Indices for Sky Imager Short Term Advective Solar forecasting.
Sheth, Nishank Mihir.
A Shadow Histogram Algorithm to Determine Clear Sky Indices for Sky Imager Short Term Advective Solar forecasting.
- 1 online resource (34 pages)
Source: Masters Abstracts International, Volume: 56-03.
Thesis (M.S.)--University of California, San Diego, 2017.
Includes bibliographical references
Sky imagers are used for short-term forecasting of solar irradiance, which can be used to counter ramp events caused by larger clouds or extensive changes in cloud cover. Sky imager forecast algorithms usually detect cloud classes in the image. However, the assignment of cloud optical depth or surface Global Horizontal Irradiance (GHI) to cloud classes remains a challenge. One method to connect GHI to cloud classes involves the use of a histogram of recently measured clear sky indices to assign a clear sky index/GHI to each cloud class. While this method improves upon choosing static GHI values for each cloud class, this paper presents a modification which improves the histogram method. Considering data from a significantly shorter time period than the existing method emphasizes more recent cloud conditions. Individual histograms for each cloud class self-consistent with modeled cloud coverage from the sky imager are used to analyze the data instead of a single histogram. The new algorithm gave a 39% reduction in root mean square error against the existing algorithm when tested over a month.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369697377Subjects--Topical Terms:
1179128
Environmental science.
Index Terms--Genre/Form:
554714
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Sky imagers are used for short-term forecasting of solar irradiance, which can be used to counter ramp events caused by larger clouds or extensive changes in cloud cover. Sky imager forecast algorithms usually detect cloud classes in the image. However, the assignment of cloud optical depth or surface Global Horizontal Irradiance (GHI) to cloud classes remains a challenge. One method to connect GHI to cloud classes involves the use of a histogram of recently measured clear sky indices to assign a clear sky index/GHI to each cloud class. While this method improves upon choosing static GHI values for each cloud class, this paper presents a modification which improves the histogram method. Considering data from a significantly shorter time period than the existing method emphasizes more recent cloud conditions. Individual histograms for each cloud class self-consistent with modeled cloud coverage from the sky imager are used to analyze the data instead of a single histogram. The new algorithm gave a 39% reduction in root mean square error against the existing algorithm when tested over a month.
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