Simulation of a CSP Solar Steam Generator, Using Machine Learning
| Autor: | Adrián González, Rogelio Peón Menéndez, Miguel Angel Vigil Berrocal, José Valeriano Álvarez Cabal, Adrian Riesgo Fernández |
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| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology Machine learning computer.software_genre Data modeling Set (abstract data type) CSP performance model data modeling Concentrated solar power 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) Block (data storage) Renewable Energy Sustainability and the Environment business.industry Aggregate (data warehouse) Process (computing) Building and Construction 021001 nanoscience & nanotechnology Support vector machine Artificial intelligence Electric power 0210 nano-technology business computer Energy (miscellaneous) |
| Zdroj: | WOS:000665995000001 Energies, Vol 14, Iss 3613, p 3613 (2021) Energies; Volume 14; Issue 12; Pages: 3613 RUO. Repositorio Institucional de la Universidad de Oviedo instname |
| ISSN: | 1996-1073 |
| Popis: | Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation. |
| Databáze: | OpenAIRE |
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