首页|Umm Al-Qura University Researchers Further Understanding of Machine Learning (A predictive machine learning model for estimating wave energy based on wave condi tions relevant to coastal regions)
Umm Al-Qura University Researchers Further Understanding of Machine Learning (A predictive machine learning model for estimating wave energy based on wave condi tions relevant to coastal regions)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news originating from Umm Al-Qura University by Ne wsRx editors, the research stated, "Growth and expansion in construction has inc reased recently and especially in coastal areas. In Alexandria, Egypt, mega proj ects 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." The news correspondents obtained a quote from the research from Umm Al-Qura Univ ersity: "Although, the great economic benefits of such projects, it will have so me negative impacts, such as overloading on the present grid. According to recom mendations of COP 27, Egypt is one of the countries targeting to increase the de pendency on green energy to minimize the production of greenhouse gases. This st udy is interested in wave energy as a renewable source of energy. Using a machin e learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa-Matruh), this study wi ll 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 1 979-2023 have been utilized for this. An extractor for wave energy can also be b uilt 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 w as 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 d etermined using a mathematical model for each of the three locations."
Umm Al-Qura UniversityCyborgsEmergin g TechnologiesMachine LearningMathematics
NETL
NSTL
