Modeling Wind Energy Using Copula

Bahraoui, Zuhair; Bahraoui, Fatima; Bahraoui, M. Amin

doi:10.4236/oalib.1104984

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…The visualization concerned helps the investigators in revealing the order and behavior of the variables, which are hard to detect when only the separate individual variables are being analyzed. The wind speed could be plotted along one axis, whereas the two additional variables could be plotted along the other axes (Bahraoui et al, 2018;Petropoulos et al, 2022). Such a scatterplot gives a good snapshot of how wind speed interrelates with their other atmospheric factors.…”

Section: Copule Analysismentioning

confidence: 99%

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Elkenawy,

Alhussan,

Eid

et al. 2024

Front. Environ. Sci.

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Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

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Section: Copule Analysismentioning

confidence: 99%

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Elkenawy,

Alhussan,

Eid

et al. 2024

Front. Environ. Sci.

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“…In his research Sklar studied the joint threedimensional distribution, and with that he introduced auxiliary functions defined in the unit support, linking the distribution function to its marginals. According to Nelsen [6], a copula is equivalent to a multivariate distribution function with uniform marginals at [0, 1], they contain the entire dependency structure between the observed random variables. This question has been widely used for bivariate cases, which is the focus of the study, given that the main objective is to observe the dependence structure between two variable variables, namely wind speed and wind power generation.…”

Section: Copula Theorymentioning

confidence: 99%

“…The dependence relationship between wind speed and wind power generation has been the focus of research such as Using Copulas for Modeling Dependence in Wind Power by the author Karakaş [5] and Modeling Wind Energy Using Copula by the author Bahraoui [6]. These studies point to the methodology of Copulas, as something innovative and efficient for the analysis of dependencies of the variables mentioned here.…”

Section: Introductionmentioning

confidence: 99%

Modeling the dependency relationship between wind speed and wind power generation: An application of copula theory

Silva¹,

Souza²,

Oliveira³

et al. 2022

REPQJ

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The concern with global warming and pollution has increased interest in the development of renewable energy sources, which are less aggressive to the environment. Wind energy can provide adequate solutions to the above-mentioned problems. The use of this energy eliminates unwanted waste that is harmful to health and the environment from other energy sources such as coal and nuclear power plants. This work aims to analyse the dependence relationship between wind speed and wind energy production, a rather complex relationship, so this study seeks to understand the stochastic nature of both phenomena. As a methodological tool the copula theory was used. A copula function is used as a general method, which consists of formulating multivariate distributions so that different dependency structures can be represented. That is, the study is based on the analysis and modelling of the dependence between wind speed data and electrical energy generation, for an hourly database of a wind farm in the state of Bahia, collected in the entire year of 2017. Thus, this paper proposes a study aiming the search for the copula function referring to the data in the period mentioned.

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