Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

作     者：Shekaina Justin Wafaa Saleh Tasneem Al Ghamdi J.Shermina 

作者机构：College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Kingdom of Saudi Arabia Transport Engineering School of Engineering and The Built EnvironmentEdinburgh Napier UniversityEdinburgh EH105DUK Department of ComputingMuscat CollegeUniversity of SterlingUK 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：1091-1109页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Deanship of Scientific Research Princess Nourah bint Abdulrahman University through the Program of Research Project Funding after publication Grand No.PRFA-P-42-16 

主　　题：Photovoltaic systems solar energy power generation prediction model deep learning 

摘      要：Renewable energy has become a solution to the world’s energy concerns in recent ***(PV)technology is the fastest technique to convert solar radiation into ***-powered buses,metros,and cars use PV *** technologies are always *** in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among *** is intermit-tent and *** forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart *** unparalleled data granularity,a data-driven system could better anticipate solar *** learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing *** article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)*** GSODBN-PPGF model predicts PV power *** GSODBN-PPGF model normalises data using data *** is used to forecast PV power *** GSO algorithm boosts the DBN model’s predicted ***-PPGF projected 0.002 after 40 h but observed *** GSODBN-PPGF model validation is compared to existing *** showed that the GSODBN-PPGF model outperformed recent *** shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.