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Hourly predictions ...
Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
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- Djaafari, Abdallah (författare)
- Laboratoire de Développement des Energies Nouvelles et Renouvelables dans les Zones Arides et Sahariennes, Faculté des Mathématiques et des Sciences de la Matière, Université Kasdi Merbah Ouargla, Ouargla, 30000, Algeria; Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Algeria
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- Ibrahim, Abdelhameed (författare)
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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- Bailek, Nadjem (författare)
- Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Algeria; Sustainable Development and Computer Science Laboratory, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, Adrar, Algeria; Engineering and Architectures Faculty, Nisantasi University, Istanbul, Turkey
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- Bouchouicha, Kada (författare)
- Centre de Développement des Energies Renouvelables, CDER, BP 62 Route de l’Observatoire, Bouzaréah 16340, Algiers, Algeria
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- Hassan, Muhammed A. (författare)
- Mechanical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, 12613, Giza, Egypt
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- Kuriqi, Alban (författare)
- CERIS, Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal; Civil Engineering Department, University for Business and Technology, 10000 Pristina, Kosovo
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- El-kenawy, El-Sayed M. (författare)
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Energy Reports. - : Elsevier. - 2352-4847. ; 8, s. 15548-15562
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Although solar energy harnessing capacity varies considerably based on the employed solar energy technology and the meteorological conditions, accurate direct normal irradiation (DNI) prediction remains crucial for better planning and management of concentrating solar power systems. This work develops hybrid Long Short-Term Memory (LSTM) models for assessing hourly DNI using meteorological datasets that include relative humidity, air temperature, and global solar irradiation. The study proposes a unique hybrid model, combining a balance-dynamic sine–cosine (BDSCA) algorithm with an LSTM predictor. Combining optimizers and predictors, such hybrid models are rarely developed to estimate DNI, especially in smaller prediction intervals. Therefore, various commonly adopted algorithms in relevant studies have been considered references for evaluating the new hybrid algorithm. The results show that the relative errors of the proposed models do not exceed 2.07%, with a minimum correlation coefficient of 0.99. In addition, the dimensionality of inputs was reduced from four variables to the two most cost-effective variables in DNI prediction. Therefore, these suggested models are reliable for estimating DNI in the arid desert areas of Algeria and other locations with similar climatic features.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- Solar energy
- Direct normal irradiation
- Concentrating solar power operation
- Algerian big south
- Extremal optimization
- Long Short-Term Memory
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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