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Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias
Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasti...
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Jeonghoon Lee, Okjeong Lee, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won, Suhyung Jang and Sangdan Kim
The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall?elevation relationship of the regional scale is inves...
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Yujie Zhang, Lei Zhang, Duo Sun, Kai Jin and Yu Gu
Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is ...
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Lei Han, Qiyan Ji, Xiaoyan Jia, Yu Liu, Guoqing Han and Xiayan Lin
Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were ...
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Jin Han and Hongmei Chang
In the context of the energy crisis and global climate deterioration, the sustainable development of clean energy will become a new direction for future energy development. Based on the development process of clean energy in China in the past ten years, ...
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The increasing share of offshore wind energy traded at the spot market requires short term wind direction forecasts to determine wake losses and increased power fluctuations due to multiple wakes in certain wind directions. The information on potential p...
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Linlin Pan, Yubao Liu, Jason C. Knievel, Luca Delle Monache and Gregory Roux
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configur...
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Junfei Qiao, Jie Cai, Honggui Han and Jianxian Cai
This study aims to develop a second order self-organizing fuzzy neural network (SOFNN) to predict the hourly concentrations of fine particulate matter (PM2.5) for the next 24 h at a regional background station called Shangdianzi (SDZ) in China from 14 to...
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Erasmo Cadenas, Wilfrido Rivera, Rafael Campos-Amezcua and Christopher Heard
Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method?s performance is well studied for a large number of prediction problems. The othe...
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Yagang Zhang, Jingyun Yang, Kangcheng Wang and Zengping Wang
This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance...
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