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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy

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dc.citedby11
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:29:53Z
dc.date.available2023-05-29T07:29:53Z
dc.date.issued2019
dc.descriptionAir quality; Decision making; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Inference engines; Mapping; Mean square error; Multiobjective optimization; Optimal systems; Particle swarm optimization (PSO); Quality control; Renewable energy resources; Solar radiation; Uncertainty analysis; Adaptive neuro-fuzzy inference system; ANFIS; Multi objective algorithm; Multi objective particle swarm optimization; Multi-objective genetic algorithm; Renewable energies; Renewable energy generation; Solar radiation forecasting; Parameter estimationen_US
dc.description.abstractThe estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash�Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2730
dc.identifier.doi10.3390/en12142730
dc.identifier.issue14
dc.identifier.scopus2-s2.0-85069612392
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069612392&doi=10.3390%2fen12142730&partnerID=40&md5=9a6c9afb596a5dbeaa405e089fae52cc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24986
dc.identifier.volume12
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleAccuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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