气候变化领域集成服务门户(CCPortal): A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions

CCPortal

DOI	10.1016/j.rineng.2023.101734
	A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions
	Hassan, Mohamed K.; Youssef, H.; Gaber, Ibrahim M.; Shehata, Ahmed S.; Khairy, Youssef; El-Bary, Alaa A.
发表日期	2024
ISSN	2590-1230
起始页码	21
卷号	21
英文摘要	Growth and expansion in construction has increased recently and especially in coastal areas. In Alexandria, Egypt, mega projects such as El -Max Port Project (Middle Port), Port of ABU QIR (EG AKI), hotels, and restaurants were spread along the coastal lines, thus, it will need a high electrical energy. Although, the great economic benefits of such projects, it will have some negative impacts, such as overloading on the present grid. According to recommendations of COP 27, Egypt is one of the countries targeting to increase the dependency on green energy to minimize the production of greenhouse gases. This study is interested in wave energy as a renewable source of energy. Using a machine learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa -Matruh), this study will try to estimate the future amount of wave energy along Egypt's coast. Hourly measurements of the significant height and the mean wave period for the period 1979-2023 have been utilized for this. An extractor for wave energy can also be built on the Overtopping Breakwater for Energy Conversion (OBREC) in order to use this energy to fill the hole in the electric grid. The machine learning model was developed using hourly wave height and period data from three buoys, and as a result, the results have a root mean square error (RMSE) of 0.52. The amount of energy taken, wave power, and system efficiency at each place were then fully determined using a mathematical model for each of the three locations. The area along the coast of Alamein had the highest energy extraction rates, followed by Alexandria and Mersa -Matruh in that order. The results of the mathematical model indicate that the yearly power generation for Alamein, Alexandria, and Mersa -Matruh is 25287 MWhr, 14713 MWhr, and 4865 MWhr, respectively.
英文关键词	Shore protection; Coastal protection; Climate change; Wave energy extractor; Renewable energy; Significant wave height; Wave period; Machine learning
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Multidisciplinary
WOS记录号	WOS:001163005900001
来源期刊	RESULTS IN ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304298
作者单位	Umm Al Qura University; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport; Egyptian Academy of Scientific Research & Technology (ASRT); Egyptian Knowledge Bank (EKB); Arab Academy for Science, Technology & Maritime Transport
推荐引用方式 GB/T 7714	Hassan, Mohamed K.,Youssef, H.,Gaber, Ibrahim M.,et al. A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions[J],2024,21.
APA	Hassan, Mohamed K.,Youssef, H.,Gaber, Ibrahim M.,Shehata, Ahmed S.,Khairy, Youssef,&El-Bary, Alaa A..(2024).A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions.RESULTS IN ENGINEERING,21.
MLA	Hassan, Mohamed K.,et al."A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions".RESULTS IN ENGINEERING 21(2024).