Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert-Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.
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http://dx.doi.org/10.1038/s41598-024-51252-y | DOI Listing |
Environ Pollut
December 2024
Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, China; Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Ningbo, China. Electronic address:
This study investigates the prevalence and impacts of suspended atmospheric microplastics (SAMPs) in the coastal metropolitan city of Ningbo in the Yangtze River Delta Region, China. The sampling was conducted at both urban centre and urban-rural fringe areas, near the coast but distant from large urban populations. SAMP abundance ranged from 0.
View Article and Find Full Text PDFSci Total Environ
December 2024
Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP; CONICET-UNPSJB), Roca 780, Esquel, Chubut CA 9200, Argentina; Facultad de Ciencias Naturales y Ciencias de la Salud, Universidad Nacional de la Patagonia San Juan Bosco, Esquel, Chubut CA 9200, Argentina. Electronic address:
Plastic pollution has garnered much more attention in marine environments, while scientific research on freshwater and terrestrial ecosystems has been relatively overlooked. Numerous studies worldwide have highlighted the presence of macroplastics (>2.5 cm) in mountain riverine environments, revealing that even these seemingly pristine ecosystems are not invulnerable to plastic contamination.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
School of Civil Engineering, Liaoning Technical University, Fuxin 123000, China; Resource utilization of coal gangue and energy-saving building materials Liaoning Provincial Key Laboratory, Liaoning Technical University, Fuxin 123000, China.
In response to the dust pollution problem in open-pit mines, an environmentally friendly network structure with a dust suppressor structure was prepared by grafting acrylamide (AM) monomers onto xanthan gum (XG). The results show that the polymer produced from 1 g XG, 15 g AM, and 0.45 g trimethylolpropane triglycidyl ether (TTE) had a more orderly structured gel with a viscosity of 81.
View Article and Find Full Text PDFJ Environ Manage
December 2024
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, 710048, China.
The construction of photovoltaic power plants (PVPPs) globally not only mitigates climate change but also exerts various impacts on terrestrial ecosystems. A comprehensive exploration of the intensity of PVPPs on the ecological environmental elements of terrestrial ecosystems, as well as their regulatory mechanisms, is an urgent scientific issue that must be addressed within the context of carbon balance. In this study, we conducted a meta-analysis to investigate the soil, climate, and biological effects of PVPPs construction, as well as changes in ecosystem CO fluxes.
View Article and Find Full Text PDFAnn Bot
December 2024
Department of Biology, Queen's University, Kingston, Ontario, K7L3N6, Canada.
Background And Aims: Seed dispersal impacts plant fitness by shaping the habitat and distribution of offspring, influencing population dynamics and spatial genetic diversity. Whether the evolution of dispersal strategies varies across herbaceous life forms (annual, perennial, clonal) is inconclusive. This study examines how seed dispersal strategies vary between annual and perennial populations of Mimulus guttatus (syn.
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