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 intermittent and weather-dependent. Accurate forecasting and modelling of PV system 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 after40 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

Control Strategies for Energy Efficiency at PNU’s Metro System

It is broadly acknowledged that there is an urgent need to reduce carbon-based mobility systems and increase renewable energy alternatives. The automotive industry is one of the greatest consumers of energy in the world. It is fronted with many challenges that aim at reducing carbon emissions. Renewable energy costs are getting cheaper and more cost effective. However, well devised design and control strategies are also needed in order to optimize any systems that are adopted in this field. Previous research shows that the energy consumption for non-traction purposes may be of the same scale as the energy used to move rolling stock, and in some cases even larger. The Kingdom of Saudi Arabia is very interested in the implementation of policies that aim at reducing energy consumption and encouraging renewable energy programs. Under its Vision 2030 development program, the Kingdom of Saudi Arabia is looking to produce 30% of its energy from renewables and other sources, with solar energy playing an important role. The work presented in this paper is aimed at an investigation of design and control strategies to reduce energy consumption and to propose a cleaner source of energy to power Princess Nourah Bint Abdulrahman University’s Automated People Mover (PNU-APM). Two areas of applications have been investigated for adopting these types of technology. Firstly, a p-v solar energy option that could be adopted for implementation in potential applications since the metro system is already in full operation using electricity. Secondly, design and control strategies including exploiting solar energy for a metro operation are discussed and investigated. A number of strategies to reduce heating, ventilation, and air conditioning (HVAC) load, which happens to be the biggest energy consumer, have been discussed. Results show great potential in energy savings with adopting p-v solar sources as well as implementation of few suggested control strategies. Some deliberations of some of the drawbacks of solar energy are also offered

The Simulation and Prototyping of a Density-Based Smart Traffic Control System for Learning Purposes

 With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient

The Simulation and Prototyping of a Density-Based Smart Traffic Control System for Learning Purposes

 With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient.</span