Urbanization and energy are intricately linked phenomena that shape the modern world. Urbanization, the process of population migration from rural to urban areas, has accelerated rapidly over the past century, driven by economic opportunities, technological advancements, and societal changes. This demographic shift has profound implications for energy consumption and sustainability. Cities, as the focal points of economic and social activity, are significant consumers of energy. The convergence of urban growth and energy demand underscores a complex nexus that influences environmental, economic, and social dynamics.The growth of urban areas often leads to increased energy needs due to the concentration of infrastructure, industries, transportation, and residential activities. Urban environments are characterized by high density, which necessitates substantial energy for heating, cooling, lighting, and powering various systems.

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together.

Introduction to Research and serves as a comprehensive guide for leveraging modern technologies in the research landscape. In an era where data generation and processing have become increasingly digital, understanding and utilizing digital tools and methods are essential for researchers across various disciplines. The proliferation of digital technologies has transformed how research is conducted, enabling more efficient data collection, enhanced data analysis, and improved dissemination of findings. Digital tools for research encompass a broad range of software and platforms designed to assist in different stages of the research process. These several data analysis, and visualization. Data collection, such as online surveys and web scraping applications, allow researchers to gather large volumes of data with unprecedented speed and accuracy. For example, platforms like SurveyMonkey or Google Forms facilitate the distribution of surveys to diverse populations, while tools and, providing insights into trends and behaviors.

Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the Archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats.