Analyzing Solar Power Plant Performance Through Data MiningSource: Journal of Solar Energy Engineering:;2008:;volume( 130 ):;issue: 004::page 44503Author:Henryk Maciejewski
,
Loreto Valenzuela
,
Manuel Berenguel
,
Jesús Fernández-Reche
,
Konrad Adamus
,
Michal Jarnicki
DOI: 10.1115/1.2969817Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This work is devoted to application of data mining methods for monitoring of state of a solar thermal plant. The methods discussed are illustrated by example of a study performed for the DISS direct steam generation facility at the Plataforma Solar de Almeria (Spain). In order to deal with the problems of large dimensionality and high correlation among the data the methods of latent variables, principal component analysis and partial least squares, were applied. Results showed that normal and abnormal states during plant operation could be identified.
keyword(s): Solar energy , Data mining , Industrial plants , Solar power stations , Steam , Principal component analysis AND Process monitoring ,
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| contributor author | Henryk Maciejewski | |
| contributor author | Loreto Valenzuela | |
| contributor author | Manuel Berenguel | |
| contributor author | Jesús Fernández-Reche | |
| contributor author | Konrad Adamus | |
| contributor author | Michal Jarnicki | |
| date accessioned | 2017-05-09T00:30:24Z | |
| date available | 2017-05-09T00:30:24Z | |
| date copyright | November, 2008 | |
| date issued | 2008 | |
| identifier issn | 0199-6231 | |
| identifier other | JSEEDO-28415#044503_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/139272 | |
| description abstract | This work is devoted to application of data mining methods for monitoring of state of a solar thermal plant. The methods discussed are illustrated by example of a study performed for the DISS direct steam generation facility at the Plataforma Solar de Almeria (Spain). In order to deal with the problems of large dimensionality and high correlation among the data the methods of latent variables, principal component analysis and partial least squares, were applied. Results showed that normal and abnormal states during plant operation could be identified. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Analyzing Solar Power Plant Performance Through Data Mining | |
| type | Journal Paper | |
| journal volume | 130 | |
| journal issue | 4 | |
| journal title | Journal of Solar Energy Engineering | |
| identifier doi | 10.1115/1.2969817 | |
| journal fristpage | 44503 | |
| identifier eissn | 1528-8986 | |
| keywords | Solar energy | |
| keywords | Data mining | |
| keywords | Industrial plants | |
| keywords | Solar power stations | |
| keywords | Steam | |
| keywords | Principal component analysis AND Process monitoring | |
| tree | Journal of Solar Energy Engineering:;2008:;volume( 130 ):;issue: 004 | |
| contenttype | Fulltext |