Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 108· 0 citations· 19 references
TL;DR
The results collectively show that the optimized ML frameworks can significantly enhance forecasting accuracy, operational efficiency, grid reliability, and utilization of renewable energy sources, establishing a robust basis for intelligent, resilient, and sustainable smart grid energy management.
Abstract
The rising need for reliable, efficient and sustainable electricity in modern power systems is stimulating the advancement of smart grid (SG) technologies. Smart grid (SG) technologies are a fast-growing area of the modern power system where their application is increasing due to the need for reliable, efficient and sustainable electricity. Renewable energy resources, distributed energy and advanced communications systems are all interconnected, making the management of energy more complex, and intelligent and optimized decision strategies are required to handle that complexity. This survey seeks to give an overview of optimized machine learning (ML) frameworks for SG energy management. It includes smart grid architecture, energy management systems, ML techniques, optimization methods and their applications, load forecasting, demand response, renewable energy integration, energy consumption optimization, fault detection, and real-time decision making. Furthermore, recent research is discussed that investigates key contributions, current constraints and new trends in research. The survey also considers some of the major challenges, including cybersecurity, scalability, data quality/computational complexity, among others, and outlines future research directions. The results collectively show that the optimized ML frameworks can significantly enhance forecasting accuracy, operational efficiency, grid reliability, and utilization of renewable energy sources, establishing a robust basis for intelligent, resilient, and sustainable smart grid energy management.
With the development of modern energy systems, there is a need to operate increasingly intelligent electricity systems. Smart grids are advanced electricity networks that allow for integrating new technologies, increasing the reliability and performance of the power system, addressing the issues of renewable energy sources, growing electricity consumption, and real-time monitoring of the operating processes. Artificial Intelligence (AI) can be applied in different areas of smart grid to make the electricity system more reliable and competitive. The methods include Machine Learning, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Fuzzy Logic, and Expert Systems. They are used to perform demand side management, predict electricity consumption, forecast renewable energy production, detect and diagnose grid failures, optimize maintenance, enhance protection and security, and manage energy consumption. However, there are challenges for implementing AI in the smart grid, such as data privacy, security, complexity, lack of transparency, and legislation. The current research explores the different aspects of applying AI technologies to smart grids. The paper provides some of the most common applications of AI and discusses the challenges and future outlook for AI-driven smart grids. The study emphasizes the significance of artificial intelligence for developing the next-generation smart grid.
A. Faiz, A. S, A. T· International Journal of Res...· 1 citation
Machine learning (ML) application in the smart grid-to-building sector presents a significant opportunity to reduce energy consumption, mitigate CO
2
emissions, and address climate change. In recent years, many reviews have explored different strategies for reducing energy consumption in buildings. However, the application of ML in smart grid-to-building systems for energy efficiency and management remains largely unexplored. This review aims to fill this gap by comprehensively analyzing various machine learning techniques, workflows and mechanisms applied in smart grid-to-buildings. A comprehensive search of three major databases, Web of Science, Google Scholar, and ScienceDirect, was conducted using various keyword combinations. The focus was on machine learning techniques like ANN, SVM, RL, and DL, as well as terms specific to smart grids and building energy management. The review highlights the significance of machine learning for load forecasting and the prediction of energy usage in buildings. Furthermore, it investigates cutting-edge modelling techniques such as digital twin technology, demonstrating its potential to contribute to energy efficiency. The review also serves as a valuable resource for future researchers aiming to optimize energy consumption using machine learning in buildings through smart grid technologies.
Mekila Mbayam Olivier, Tijani Bounahmidi· Journal of Green Building, C...· 0 citations
This study proposes a novel AI-powered smart grid management framework that integrates predictive analytics, machine learning, and adaptive control techniques to optimize energy distribution, minimize transmission losses, and improve overall cost efficiency. The proposed system utilizes a comprehensive dataset comprising energy consumption, energy generation, voltage, current, temperature, wind speed, solar irradiance, battery storage, and dynamic electricity pricing to develop an intelligent decision-making architecture. Advanced machine learning algorithms are employed for energy demand forecasting, power flow optimization, and loss minimization, thereby enhancing grid efficiency, reliability, and renewable energy integration. The results demonstrate that AI-based optimization significantly improves grid resilience, load balancing, adaptive pricing strategies, and operational efficiency, contributing to the development of scalable, intelligent, and sustainable smart grid systems for future energy management.
Sujata Hanumant Kale· Dandao Xuebao/Journal of Bal...· 0 citations
The growing energy demand, acceleration of urbanization and expansion of industrial operations have raised the demand for intelligent energy management strategies for achieving energy efficiency and cost savings while minimizing carbon emissions. Most traditional energy management systems are based on rule based control structures and statistical methods that are not able to adjust to dynamic occupancy patterns, varying environmental conditions, and complex industrial processes. In recent years, a new technique, namely Machine Learning (ML), has emerged as a viable solution to predict, optimize, and automatically control energy use in smart buildings and industrial systems. The paper examines the various ML techniques for energy optimization in detail, and categorizes them into five areas: energy optimization for HVAC systems, energy optimization for lighting control, integration of renewable energy systems, energy optimization for industrial processes, and predictive maintenance. The proposed framework involves the combination of IoT sensors, real-time data collection, data preprocessing, feature engineering, development of ML models, and optimization algorithms, all aimed at realizing intelligent energy management. A set of supervised, unsupervised, deep learning, and reinforcement learning algorithms is examined, such as Random Forest, Support Vector Machine, XGBoost, Artificial Neural Network, Long Short-Term Memory network and Deep Reinforcement Learning with regard to their prediction accuracy, computational efficiency and energy saving requirement. As shown in the comparative analysis, advanced ML models consistently outperform traditional methods in predicting energy consumption, detecting consumption trends and reducing equipment idle time, as well as optimizing operational schedules. The results show that an energy optimization approach based on ML can greatly contribute to energy efficiency, operational cost savings, comfort of the occupants, and sustainable production in industry. The paper also presents the challenges and opportunities that are currently being faced, such as data quality, model interpretability, scalability, cybersecurity, and real-time deployment, and suggests future research directions, including the application of explainable AI, edge computing, digital twins, federated learning and autonomous energy management systems. In this study, the researchers give an overview of emerging ML techniques and practical lessons to the researchers and practitioners to develop intelligent, scalable and sustainable solutions for energy optimization in next-generation smart buildings and industrial facilities.
Prince Raj, Ankur Priyadarshi, Rajesh Kumar et al.· International journal of com...· 0 citations
Hybrid Renewable Energy Systems (HRES) have emerged as an effective solution for addressing the increasing global demand for clean, reliable, and sustainable energy while reducing dependence on fossil fuels. By integrating multiple renewable energy sources such as solar photovoltaic (PV), wind turbines, biomass, and small hydropower with energy storage systems and intelligent energy management strategies, HRES can overcome the intermittent nature of individual renewable resources and ensure continuous power supply. This paper presents a comprehensive hybrid renewable energy framework that combines renewable generation, battery energy storage, smart power converters, and IoT-enabled monitoring for efficient energy utilization in residential, commercial, and remote applications. The proposed system employs intelligent control algorithms to optimize power generation, storage scheduling, and load distribution under varying environmental and load conditions. Real-time monitoring and predictive energy management improve system reliability, reduce operational costs, and enhance grid stability while minimizing carbon emissions. The integration of artificial intelligence and optimization techniques further enables adaptive decision-making, fault detection, and energy forecasting, leading to higher overall system efficiency and resilience. Simulation-based performance evaluation demonstrates that the proposed HRES achieves improved renewable energy utilization, enhanced power quality, reduced energy losses, and increased system reliability compared with conventional standalone renewable energy systems. The proposed architecture offers a scalable, cost-effective, and environmentally sustainable solution for future smart grids and decentralized energy systems.
Keywords— Hybrid Renewable Energy System (HRES), Solar Photovoltaic, Wind Energy, Battery Energy Storage System (BESS), Smart Grid, Energy Management System (EMS), Internet of Things (IoT), Artificial Intelligence (AI), Renewable Energy Integration, Power Quality, Energy Optimization, Sustainable Energy.
Bandi Sruthi, Dhontha Sindhuja, P. Kavitha· International Journal of Cre...· 0 citations
Abstract. The growing presence of renewable energy sources into manufacturing systems presents massive challenges in their intermittency and uncertainty, causing inefficiency in energy consumption and production planning. The paper introduces a smart energy management network of manufacturing systems that dynamically balances machine activities with the availability of renewable energy. The suggested solution consists of a predictive model of renewable generation and an optimization-based scheduling system to reduce the cost of energy, delays in production, and grid reliance. There is a multi-objective formulation that is created based on energy use, operating limitations and emission parameters. A smart decision-making policy, founded on machine learning-aided prediction and adaptive scheduling, is applied to guarantee real-time responsiveness when operating in different energy conditions. The framework is tested on a realistic load and renewable profile on a representative manufacturing scenario. Findings show that there are considerable increases in the use of renewable energy, decrease in peak grid demand, and general energy cost savings over traditional scheduling methods. The new methodology provides a scalable and viable approach to sustainable and energy-efficient smart manufacturing systems.
A. R· Materials Research Proceedin...· 0 citations