2022 JUN 09 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- Research findings on Mathematics are discussed in a new report. According to news reporting originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “There are several major available renewable energies, such as wind power which can be considered one of the most potential energy resources. Thus, wind power is a vital green source of electric power generation.”
Financial support for this research came from National Key Research and Development Program of China.
Our news editors obtained a quote from the research from Wuhan University, “The prediction of wind power is a critical issue to decrease the uncertainty of the energy systems. It is an essential process to balance energy demand and supply. The main objective of the current paper is to present an efficient prediction tool to estimate wind power using time-series datasets. We develop an enhanced variant of the ANFIS (adaptive neuro-fuzzy inference system) using the advances of metaheuristic (MH) optimization algorithms. We propose a new variant of the marine predator algorithm (MPA), called MPAmu, using additional mutation operators to augment the MPA to prevent its premature convergence on local optima. The developed MPAmu is used to optimize the ANFIS parameters and to boost its configuration process. We use well-known datasets collected from wind turbines located in France to evaluate the proposed MPAmu-ANFIS model using several evaluation metrics. Additionally, we compare the developed MPAmu-ANFIS to the traditional ANFIS and several modified ANFIS models using different MH algorithms. More so, we compare the developed model to other time-series prediction models, such as support vector machine (SVM), feedforward neural network, and long short term memory (LSTM).”
According to the news editors, the research concluded: “The findings of the current paper reveal that the application of MPAmu contributes significantly to boosting the prediction accuracy of the traditional ANFIS.”
This research has been peer-reviewed.
For more information on this research see: Boosted Anfis Model Using Augmented Marine Predator Algorithm With Mutation Operators for Wind Power Forecasting. Applied Energy, 2022;314. Applied Energy can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Applied Energy - http://www.journals.elsevier.com/applied-energy/)
The news editors report that additional information may be obtained by contacting Mohammed A. A. Al-qaness, Wuhan University, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, People’s Republic of China. Additional authors for this research include Hong Fan, Ahmed A. Ewees, Laith Abualigah and Mohamed Abd Elaziz.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.apenergy.2022.118851. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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