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Abdullah F. Al-Aboosi, Aldo Jonathan Muñoz Vazquez, Fadhil Y. Al-Aboosi, Mahmoud El-Halwagi and Wei Zhan
Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intellig...
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Chih-Chiang Wei and Cheng-Shu Chiang
In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world?s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize off...
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Dibo Dong, Shangwei Wang, Qiaoying Guo, Yiting Ding, Xing Li and Zicheng You
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network?long short-term memory joint fr...
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Xing-Zhou Li, Zhong-Ren Peng, Qingyan Fu, Qian Wang, Jun Pan and Hongdi He
Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world?s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essentia...
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Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term ave...
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Tianhao Wang, Hongying Meng, Rui Qin, Fan Zhang and Asoke Kumar Nandi
Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in fa...
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Dibo Dong, Shangwei Wang, Qiaoying Guo, Xing Li, Weibin Zou and Zicheng You
Accurately predicting wind speed is crucial for the generation efficiency of offshore wind energy. This paper proposes an ultra-short-term wind speed prediction method using a graph neural network with a multi-head attention mechanism. The methodology ai...
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Xuguo Jiao, Daoyuan Zhang, Dongran Song, Dongdong Mu, Yanbing Tian and Haotian Wu
As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, a...
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Francesco Granata, Fabio Di Nunno, Mohammad Najafzadeh and Ibrahim Demir
A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the...
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Ryunosuke Komura and Masayuki Matsuoka
Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a ...
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