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

An Investigation into Conversion of a Fleet of Plug-in-Electric Golf Carts into Solar Powered Vehicles Using Fuzzy Logic Control

This paper presents an investigation factors that need to be considered in the design and selection of components for the conversion of a fleet of plug-in electric golf carts at Princess Nourah Bint Abdelrahman University, (PNU), Riyadh, Kingdom of Saudi Arabia (KSA), into solar power energy. Currently, the plug-in electric golf carts are powered by a set of deep-cycle lead-acid battery packs consisting of six units. Solar energy systems (photovoltaics and solar thermal) provide significant environmental benefits and opportunities over the traditional and conventional sources. Therefore, they can contribute positively to many aspects of the built environment and societies. There are many factors that affect the energy generated from the solar panel system. These include type and dimension of the solar panels, weight, speed, acceleration, and other characteristics of the used golf carts, and the energy efficiency of the solar energy system, as main factors that affect the green energy generated to operate the carts. The energy values needed to power the electric cart were calculated and optimized using traction energy calculation and optimized using a fuzzy logic analysis. The fuzzy logic system was developed to assess the impacts of varying dimensions of solar panel, vehicle speed, and weight on the energy generation. Initial calculations show that the replacement cost of the batteries can be up to approximately 75 percent of the operating cost. Together with the indirect cost benefits of achieving zero tail-pipe emission and the comfort of silent operation, the cost of operation using solar energy can be significant when compared with the cost of battery replacement. In order to achieve better efficiency, supercapacitors can be investigated to replace the conventional batteries. The use of fuzzy logic successfully facilitated the optimization of system operation conditions for best performance. In this study, fuzzy logic and calculated data were used as an optimization tool. Future work may be able to use fuzzy logic with experimental data to demonstrate feasibility of utilizing fuzzy logic systems to assess energy generation processes. Future investigations could also include investigation of other factors and methodologies, such as various types of batteries, supercapacitors, solar panels, and types of golf carts, together with different techniques of artificial intelligence to assess the optimum system specifications

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

Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

Climate control in a pixel non-uniformity metro system includes regulating the air, humidity, and temperature quality within metro trains and stations to ensure passenger comfort and safety. The climate control system in a PNU metro system combines intelligent algorithms, energy-efficient practices, and advanced technologies to make a healthy and comfortable environment for passengers while reducing energy consumption. The proposed an automated climate control using an improved salp swarm algorithm with an optimal ensemble learning technique examines the underlying factors, including indoor air temperature, wind direction, indoor air relative humidity, light sensor 1 (wavelength), return air relative humidity, supply air temperature, wind speed, supply air relative humidity, airflow rate, and return air temperature. Moreover, this new proposed technique applies ISSA to elect an optimal set of features. Then, the climate control process takes place using an ensemble learning approach comprising long short-term memory, gated recurrent unit, and recurrent neural network. Lastly, the Harris hawks optimization algorithm can be employed to adjust the hyperparameters related to the ensemble learning models. The extensive results demonstrated the supremacy of the proposed algorithms over other approaches to the climate control process on PNU metro systems