Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

•A solar dish/Stirling power plant was modeled using artificial intelligence models.•A hybrid random vector functional link (RVFL) models were developed.•Four meta-heuristic optimizers were used to find the optimum structure of RVFL.•A chimp optimization algorithm (CHOA) was declared as the best opt...

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Vydáno v:Solar energy Ročník 222; s. 1 - 17
Hlavní autoři: Zayed, Mohamed E., Zhao, Jun, Li, Wenjia, Elsheikh, Ammar H., Elaziz, Mohamed Abd, Yousri, Dalia, Zhong, Shengyuan, Mingxi, Zhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.07.2021
Pergamon Press Inc
Témata:
Algorithms
Artificial intelligence
Chimp optimization algorithm
Computer applications
Heuristic methods
Hybrid systems
Optimization algorithms
Optimization models
Performance prediction
Power plants
Power production prediction
Prediction models
Random Vector Functional Link
Soft computing
Software
Solar collectors
Solar dish Stirling power plant
Solar energy
Spherical search optimization
Statistical analysis
Statistics
Swarm intelligence
Whale optimization algorithm
Spherical search optimization
Power production prediction
Random Vector Functional Link
Chimp optimization algorithm
Whale optimization algorithm
Solar dish Stirling power plant
ISSN:0038-092X, 1471-1257
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Shrnutí:•A solar dish/Stirling power plant was modeled using artificial intelligence models.•A hybrid random vector functional link (RVFL) models were developed.•Four meta-heuristic optimizers were used to find the optimum structure of RVFL.•A chimp optimization algorithm (CHOA) was declared as the best optimizer.•Several statistical tools were used to assess the accuracy of the proposed models.•RVFL-CHOA had a superior accuracy in predicting the electrical power of the plant. Hybrid artificial intelligence models have become promising tools for soft computing and computational intelligence, as they can deal with complicated sustainable systems such as the prediction modeling of concentrated power systems. In these models, one or two artificial intelligence techniques are integrated with an optimization algorithm to develop a fine-tuned prediction modeling. In this paper, we develop a novel hybrid prediction model using an improved version of the Random Vector Functional Link (RVFL) network to predict the instantaneous output power and the monthly power production of a solar dish/Stirling power plant (SDSPP). A new metaheuristic algorithm called Chimp Optimization Algorithm (CHOA) has been combined with the RVFL network to effectively determine the optimal values of RVFL parameters. More so, the proposed RVFL-CHOA model is compared with four artificial-based models include the original RVFL, and three hybrid modified versions of the RVFL model using the Particle Swarm Optimization (PSO), Spherical Search Optimization (SSO), and Whale Optimization Algorithm (WOA). The prediction performance of the five models was compared using various statistical evaluation metrics. The statistical results prove the superiority and effectiveness of the proposed RFVL-CHOA method among the other investigated optimized models for performance prediction of the SDSPP. Based on the test data, the REVL-CHOA predicts the instantaneous output power and the monthly power production of the SDSPP with determination coefficient values of 0.9992, and 0.9108, and root mean square error values of about 0.00047, and 0.05995, respectively.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2021.03.087