Urban monitoring in 6th Generation (6 G) networks is vital for ensuring smart city security and efficiency. Traditional methods rely on either standalone Unmanned Aerial Vehicles (UAVs) or Unmanned Ground Vehicles (UGVs), often suffering from limited coverage, intermittent connectivity, and inefficient energy management. Recent works have explored UAV-UGV collaboration to enhance surveillance and communication; however, they lack dynamic communication optimization and energy-efficient coordination. To address these gaps, we propose a novel cooperative framework integrating UAVs equipped with Reconfigurable Intelligent Surfaces (RIS) and UGVs for real-time monitoring. Unlike prior approaches, our system optimizes UAV flight paths and recharging schedules using Deep Reinforcement Learning (DRL) while refining UGV patrol routes with a Genetic Algorithm (GA), ensuring adaptive and continuous surveillance. Additionally, we employ Differential Evolution (DE) for RIS configuration, enhancing data transmission and mitigating urban signal degradation. UAVs further support UGVs by wirelessly recharging them via energy beamforming, reducing dependency on fixed charging stations. By leveraging AI-driven coordination, RIS-assisted communication, and real-time energy optimization, our framework ensures seamless data transmission, reduces latency, and maximizes energy efficiency. Simulation results demonstrate that our approach significantly improves communication reliability, monitoring coverage, and energy consumption compared to existing methods, making it a promising solution for next-generation urban monitoring.

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability. The provision of sustainable Resource Allocation (RA) and ensure real-time data-processing interaction between Internet of Things (IoT) devices and edge servers depends on a balance between system latency and Energy Consumption (EC) based on the proposed DT-empowered Deep Reinforcement Learning (Deep-RL) agent. The Deep-RL agent evaluates the performance action based on RA actions in DT to distribute its bandwidth resources to IoT devices based on iteration and the actions taken to generate the best policy and enhance learning efficiency at every step. The simulation results show that the proposed Deep-RL-agent-based DT is able to exploit the best policy, select 47.5% of computing activities that are to be carried out locally with 1 MHz bandwidth and minimize the weighted cost of the transmission policy of edge-computing strategies.

As decentralized energy systems gain momentum, microgrids (MGs) have become a vital component of the modern power landscape. Yet, maintaining power quality (PQ) within these systems presents ongoing challenges due to the presence of nonlinear loads, variable renewable energy sources, and frequent switching operations. These factors contribute to PQ disturbances, such as harmonic distortion, voltage instability, and synchronization issues. Conventional mitigation methods often struggle to cope with such dynamic and complex environments. This review investigates the emerging role of artificial intelligence (AI) as a powerful tool for optimizing PQ in MGs. It presents a detailed overview of various AI‐based methods, including machine learning (ML), metaheuristics, deep learning, fuzzy logic, and hybrid approaches and their implementation in areas like harmonic suppression, voltage and frequency regulation, islanding detection, renewable energy coordination, and predictive diagnostics. The study evaluates these techniques based on key performance indicators, such as precision, scalability, and suitability for real‐time operation, while also addressing challenges related to data reliability, interpretability, and cybersecurity. The article concludes by highlighting future research directions, such as AI integration with Internet of Things (IoT), edge computing, and decentralized intelligence. Overall, the review illustrates how AI can play a pivotal role in transforming MG PQ optimization for the evolving smart grid era.