Dependence of daily precipitation and wind speed over coastal areas: evidence from China's coastline

Qi, Xuyang; Qian, Siwei; Chen, Kebing; Li, Jun; Wu, Xushu; Wang, Zhaoli; Deng, Zifeng; Jiang, Jie

doi:10.2166/nh.2023.093

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Coastal areas have specific wind characteristics. The consistent and strong winds commonly found in coastal regions create favorable conditions for efficient energy production [7], [8], [9]. Furthermore, the proximity to the coastline allows for the installation of offshore wind power plants, further expanding the potential of wind energy.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation for Wind Power Plant Based on Wind Potency in Coastal Area Using Matlab Simulink: Case Study in Cilacap, Indonesia

Saputri,

Pranata

2024

Advances in Transdisciplinary Engineering

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Wind power plants are one of the power generators that utilize renewable energy, thereby reducing carbon dioxide emissions. As the world seeks to mitigate the impacts of climate change, wind energy emerges as a promising solution due to its environmentally friendly nature and its potential for power generation on both small and large scales. In Indonesia, specifically in Cilacap as a coastal area, the wind potential allows for the establishment of a wind power plant. This research aims to model and analyze a wind power plant based on the wind potential in coastal areas using Matlab Simulink, with a case study in Cilacap, Indonesia. The method utilized involves problem identification, collecting technical data from Cilacap, and data regarding turbine components. The modeling is performed using Matlab Simulink. The results from the modeling and simulation yield a mechanical power output of 221.9 Watts and a torque of 6.448 Nm by using the horizontal axis wind turbine model ATO-WT-1000M5. The outcomes are recommended for small-scale electricity usage.

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Section: Introductionmentioning

confidence: 99%

Modeling and Simulation for Wind Power Plant Based on Wind Potency in Coastal Area Using Matlab Simulink: Case Study in Cilacap, Indonesia

Saputri,

Pranata

2024

Advances in Transdisciplinary Engineering

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Significant influence of urban human activities and marine input on rainwater chemistry in a coastal large city, China

Zeng,

Han,

et al. 2024

Water Research

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Interpretable Transformer Neural Network Prediction of Diverse Environmental Time Series Using Weather Forecasts

Orozco López,

Kaplan,

Linhoss

2024

Water Resources Research

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Transformer neural networks (TNNs) have caused a paradigm shift in deep learning domains like natural language processing, gathering immense interest due to their versatility in other fields such as time series forecasting (TSF). Most current TSF applications of TNNs use only historic observations to predict future events, ignoring information available in weather forecasts to inform better predictions, and with little attention given to the interpretability of the model's use of explanatory inputs. This work explores the potential for TNNs to perform TSF across multiple environmental variables (streamflow, stage, water temperature, and salinity) in two ecologically important regions: the Peace River watershed (Florida) and the northern Gulf of Mexico (Louisiana). The TNN was tested and its prediction uncertainty quantified for each response variable from one‐to fourteen‐day‐ahead forecasts using past observations and spatially distributed weather forecasts. A sensitivity analysis (SA) was performed on the trained TNNs' attention weights to identify the relative influence of each input variable on each response variable across prediction windows. Overall model performance ranged from good to very good (0.78 < NSE < 0.99 for all variables and forecast horizons). Through the SA, we found that the TNN was able to learn the physical patterns behind the data, adapt the use of input variables to each forecast, and increasingly use weather forecast information as prediction windows increased. The TNN's excellent performance and flexibility, along with the intuitive interpretability highlighting the logic behind the models' forecasting decision‐making process, provide evidence for the applicability of this architecture to other TSF variables and locations.

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Dependence of daily precipitation and wind speed over coastal areas: evidence from China's coastline

Cited by 3 publications

References 41 publications

Modeling and Simulation for Wind Power Plant Based on Wind Potency in Coastal Area Using Matlab Simulink: Case Study in Cilacap, Indonesia

Modeling and Simulation for Wind Power Plant Based on Wind Potency in Coastal Area Using Matlab Simulink: Case Study in Cilacap, Indonesia

Significant influence of urban human activities and marine input on rainwater chemistry in a coastal large city, China

Interpretable Transformer Neural Network Prediction of Diverse Environmental Time Series Using Weather Forecasts

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