Comparative evaluation of optimal Weibull parameters for wind power predictions using numerical and metaheuristic optimization methods for different Indian terrains

Patidar, Harsh; Shende, Vikas; Baredar, Prashant; Soni, Archana

doi:10.1007/s11356-022-24395-6

Cited by 13 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to authors there has been a yearly installation of more than 50 GW of wind farms since 2014 (Katsigiannis and Stavrakakis, 2014Mattar and Guzmán-Ibarra, 2017). Global wind speed was opined to have between 93 to 95 GW new installed wind capacity in 2020 and 94 GW in 2021 (GWEC, 2023; Patidar et al, 2023). In 2022 new global wind capacity fell to 78 GW, a 17% decline owing to 5% loss in markets share amongst two of the largest wind market, China and USA (GWEC, 2023).…”

Section: Wind Energy Capacitymentioning

confidence: 99%

Nigerian wind energy status

Akporhonor,

Otuagoma,

Akporhonor

2023

Wind Engineering

View full text Add to dashboard Cite

This review paper evaluates the current status of the Nigeria wind energy sector to identify the available Wind Energy Potential, Installed Wind Energy Capacity and Renewable Energy Policies in a bid to provide accurate information to aid the exploration and exploitation of wind energy across Nigeria. Consequently, the huge expanse between the wind energy Potential across Cities in Nigeria, the poor numbers of installed wind capacity and Nigeria’s Renewable Energy policies show low interest in Wind Energy in comparison to other RE sources. All this calls for further investment in the Nigerian wind energy sector

show abstract

Section: Wind Energy Capacitymentioning

confidence: 99%

Nigerian wind energy status

Akporhonor,

Otuagoma,

Akporhonor

2023

Wind Engineering

View full text Add to dashboard Cite

show abstract

“…Additionally, artificial intelligence (AI) methods, primarily driven by heuristic algorithms, have demonstrated superiority over other methods in terms of solution accuracy and wind speed characterization [27][28][29][30][31][32]. In this category, the Cuckoo Search (CS) algorithm was the most frequently used [33,34] followed by, in some cases, the genetic algorithm (GA) [35][36][37]. Figure 2 shows the share of each parameter estimation method in 46 studies reviewed by Jung and Schindler [38].…”

Section: Introductionmentioning

confidence: 99%

“…Forecasting 2024, 6, FOR PEER REVIEW 3 the genetic algorithm (GA) [35][36][37]. Figure 2 shows the share of each parameter estimation method in 46 studies reviewed by Jung and Schindler [38].…”

Section: Introductionmentioning

confidence: 99%

Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model

Younis,

Belabbes,

Cotfas

et al. 2024

Forecasting

View full text Add to dashboard Cite

This study introduces a novel adjustment to the firefly algorithm (FA) through the integration of rare instances of cannibalism among fireflies, culminating in the development of the honeybee mating-based firefly algorithm (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served as a rigorous testing ground to evaluate the efficacy of the new algorithm in diverse optimization scenarios. Moreover, thorough statistical analyses, including two-sample t-tests and fitness function evaluation analysis, the algorithm’s optimization capabilities were robustly validated. Additionally, the coefficient of determination, used as an objective function, was utilized with real-world wind speed data from the SR-25 station in Brazil to assess the algorithm’s applicability in modeling wind speed parameters. Notably, HBMFA achieved superior solution accuracy, with enhancements averaging 0.025% compared to conventional FA, despite a moderate increase in execution time of approximately 18.74%. Furthermore, this dominance persisted when the algorithm’s performance was compared with other common optimization algorithms. However, some limitations exist, including the longer execution time of HBMFA, raising concerns about its practical applicability in scenarios where computational efficiency is critical. Additionally, while the new algorithm demonstrates improvements in fitness values, establishing the statistical significance of these differences compared to FA is not consistently achieved, which warrants further investigation. Nevertheless, the added value of this work lies in advancing the state-of-the-art in optimization algorithms, particularly in enhancing solution accuracy for critical engineering applications.

show abstract