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

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included 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 others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It 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.

Driving Cycle for Motorcycle Using Micro-Simulation Model

Driving cycle of vehicle has been used in emission estimation and fuel consumption study. Existing method of data collection using car chasing technique is expensive. The technique using micro simulation approach is cheaper and fast to derive the driving cycle. In this paper a traffic simulation model Driving Cycle Micro-Simulation Model for Motorcycle has been developed. The issue of lateral and longitudinal movement aspect in motorcycle driving has been examined in the model. Parameters to cover such movement have been built in the model and applied on a stretch in Edinburgh city of Scotland. Results from model have been both calibrated and validated. The results show that Driving Cycle Micro-Simulation Model for Motorcycle gives better representation of driving cycle and it can be used to understand the effect of driving modes on emission for better understanding of vehicular emission control.