Impacts of flexible renewable hybrid system with electric vehicles considering economic reactive power management on microgrid voltage stability and operation.

This article delves into the eco-friendly operation of a smart microgrid, highlighting its ability to maintain voltage security through a flexible renewable hybrid system. The framework incorporates wind and bio-waste energy sources to produce electricity, while leveraging electric vehicles as mobile storage units and flexibility resources. The hybrid system is also capable of managing reactive power.

Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study.

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential and commercial sectors, integrating renewable energy in building systems presents significant challenges. This is particularly evident in cold regions where unpredictable resource availability complicates energy reliability.

Optimization of nano-finned enclosure-shaped latent heat thermal energy storage units using CFD, RSM, and enhanced hill climbing algorithm.

Thermal energy storage plays a critical role in improving energy efficiency and sustainability, particularly in solar energy systems, industrial waste heat recovery, and building temperature regulation. However, traditional latent heat thermal energy storage (LHTES) systems face significant challenges due to the low thermal conductivity of phase change materials (PCMs), leading to prolonged charging/discharging times and reduced efficiency. To address these limitations, this study presents a framework for optimizing nano-finned enclosure-shaped LHTES units that incorporate nano-enhanced phase change materials (NePCMs) and fins.

Enhancing Intelligent HVAC optimization with graph attention networks and stacking ensemble learning, a recommender system approach in Shenzhen Qianhai Smart Community.

This study details the design and implementation of an intelligent HVAC optimization system in the Shenzhen Qianhai Smart Community, utilizing advanced machine learning methods like Graph Attention Networks (GATs) and stacking ensemble learning. A comprehensive sensor network monitored temperature, humidity, occupancy, and air quality, allowing for real-time data collection and responsive control. Data preprocessing involved Z-score normalization and feature engineering to improve model accuracy.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy.

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids.

Usage of double injector for efficient mixing of the fuel behind the ramp injector at supersonic combustion chamber.

This investigation presents extensive computational analyses of the compressible flow near ramp injector with double circular injectors at supersonic combustor of scramjet engine. Comparison of the fuel mixing and fuel jet penetration of hydrogen jet are done for two injector configurations at free stream Mach number of 2. The simulation of the supersonic flow near ramp injector is done via solving RANS equations with computational fluid dynamic technique.

Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems.

Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature.