The Neural Network Classifier Works Efficiently on Searching in DQN Using the Autonomous Internet of Things Hybridized by the Metaheuristic Techniques to Reduce the EVs’ Service Scheduling Time

Abstract

Since the rules and regulations strongly emphasize environmental preservation and greenhouse gas GHG reduction, researchers have progressively noticed a shift in the transportation means toward electromobility. Several challenges must be resolved to deploy EVs, beginning with improving network accessibility and bidirectional interoperability, reducing the uncertainty related to the availability of suitable charging stations on the trip path and reducing the total service time. Therefore, suggesting DQN supported by AIoT to pair EVs’ requests and station invitations to reduce idle queueing time is crucial for long travel distances. The author has written a proposed methodology in MATLAB to address significant parameters such as the battery charge level, trip distance, nearby charging stations, and average service time. The effectiveness of the proposed methodology is derived from hybridizing the meta-heuristic techniques in searching DQN learning steps to obtain a solution quickly and improve the servicing time by 34%, after solving various EV charging scheduling difficulties and congestion control and enabling EV drivers to policy extended trips. The work results obtained from more than 2145 training hypothetical examples for EVs’ requests were compared with the Bayesian Normalized Neural Network (BASNNC) algorithm, which hybridize the Beetle Antennae Search and Neural Network Classifier, and with other methods such as Grey Wolf Optimization (GWO) and Sine-cosine and Whale optimization, revealing that the mean overall comparison efficiencies in error reduction were 72.75%, 58.7%, and 18.2% respectively.

Accurate Identification of Harmonic Distortion for Micro-Grids Using Artificial Intelligence-Based Predictive Models

“…The three primary neural electors that make up an ANNs technique are the input, hidden neurons, and output layer [3]. Inspired by the human brain, the ANN's components function in parallel.…”

“…However, there is a primary obstacle, which is Total Harmonic Distortion (THD) under different operating conditions is not treated, which neglects variations and uncertainties at the network level. Therefore, the suggested technique will be called Online harmonic identification technique (HIT) -in microgrid [3].…”

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

“…In the 21st century, most of the current studies adopt an innovative improvement of neural networks by interfering with one of the metaheuristic methods such as the gravitational emulation local search and applied in the electricity-producing optimization in 2010 [65]. In the case of the multi-objective optimization model using the linear decreasing particle swarm optimization algorithm, which is recommended and advised with using the weighted superposition attraction algorithm (WSA) when a solution must be chosen in a minimum time for problems that have multi-pass [66], which was used for the parameter selection and appeared excellent over the native PSO. This thinking approach of the "need-based" must be a guide matched with the understanding of the behavior of the operating parameters through real operating, mainly if applied considering multiple constraints.…”

“…These goals are divided into two subobjectives. The first is tracking the cracks' creation positions, P i,j , and their intensity, Q P [66]. The neural network extracts data images (P i,j , Q P , r, ω, h r , R cr , R 0 , d, , L i , d sr ) through many iterations approximate to 1000 to obtain less deviation.…”