Simulation and modeling in cloud computing-based smart grid power big data analysis technology

This paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.

Guest Editorial: Situational awareness of integrated energy systems

To fulfill the fluctuating demands of energy, there is dire need of energy management alongside the production of electrical energy. A number of management tools such as home energy management, demand side management and demand response are in practice in many electric power systems. Energy consumption is a real time activity therefore there is need of real time decisions. For real time decisions, the big data generated from energy management tools need to be processed in real time. Big data cannot be handled through traditional systems therefore there is dire need of efficient, reliable, scalable and cost effective system. Cloud computing provides the said required resources on demand. In this paper we simulated the use of cloud computing environment in smart grid. Two service broker policies are compared in term of processing time, response time, requests per hour and cost. To control congestion on network, a collaborative model for home to building communication is also proposed.

Energy management has enhanced sustainability, dependability, and efficiency in smart grids. Urbanisation, technology, and consumer behaviour have boosted need for innovative power use and price control systems. The paper intends to construct ML for smart grid power use and price prediction. This work used an advanced shark smell-tuned flexible support vector machine (ASS-FSVM) to forecast smart grid price and power use. Weather stations, smart meters, and market price databases document power use and pricing. The quality and consistency of data are enhanced via the processes of cleaning and normalizing inputs. PCA reduces dimensionality by extracting pre-processed data characteristics. Optimized and tested FSVM models can anticipate smart grid power use and pricing. ASS may identify the most important dataset properties. The research evaluates electricity consumption forecasting using accuracy (98.05%), recall (98.93%), precision (97.10%), and F1-score (98.04%), and electricity price predicting using MAPE (4.32%), RMSE (5.80%), MSE (8.50%), and MAE (2.95%). The recommended strategy greatly increases forecast accuracy, helping utilities improve grid stability, demand responsiveness, and customer pricing.

This study addresses the persistent problem of thermal stress, electrical losses, and premature failures in power distribution transformers operating in smart grid environments, where dynamic loading and high reliability expectations intensify asset risk. The purpose is to quantify how smart grid oriented operational, monitoring, and maintenance practices enhance transformer thermal and electrical performance and reliability. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 engineers, operators, maintenance supervisors, and asset managers across eight smart grid enabled utility and industrial enterprises, using a five-point Likert scale instrument with construct reliabilities between 0.86 and 0.91. Key independent variables were smart monitoring and automation, thermal management and cooling practices, load management and demand response, and predictive maintenance and diagnostics, while dependent variables captured perceived thermal performance, electrical performance and efficiency, and reliability and lifespan outcomes. Data were analyzed using descriptive statistics, Pearson correlations, and multiple regression. Headline findings show all practice constructs are positively and significantly related to performance, with correlations between 0.53 and 0.71 and regression models explaining 53–60 percent of variance in outcomes; predictive maintenance (β = 0.38, p < .001) and smart monitoring (β = 0.30–0.31, p < .001) emerge as the strongest predictors of reliability and thermal performance, while thermal management most strongly predicts electrical efficiency (β = 0.34, p < .001). Organizations with more than five years of smart grid deployment report significantly higher practice levels and reliability scores (mean reliability 4.05 versus 3.71, p = .001). The findings imply that integrated investment in smart monitoring, disciplined thermal and load management, and predictive maintenance is critical for extending transformer life and improving smart grid resilience.

With the development of the times and the progress of society, the development and change of the demand response potential of China’s residents are facing unprecedented challenges. In today’s big data era, the combination of big data technology and the potential analysis of demand response of China’s residential users has become the inevitable demand of the development of the times. Therefore, in order to better make the demand potential of Chinese residents conform to the development trend of the times, this paper deeply studies the business development trend and status quo of the Internet in the demand analysis and response of residents in recent years through the technology of Internet and big data, and analyzes the potential of demand analysis and response of residents in recent years, A large number of information resources about the demand analysis and response of residential users in the new Internet era are sorted out, and the business fields of residents’ demand analysis and response are re classified. The evaluation model of influencing factors of user demand response behavior is established, and the Monte Carlo simulation calculation method is used for research. It is found that time and price is the main factors influencing the demand response behavior of typical industries. Through the analysis, the accuracy rate of the big data analysis method proposed in this paper reaches 97.3% in studying the potential of residents’ demand response.

In this paper, we propose a dynamic event-based distributed reinforcement learning algorithm to address the resource allocation problem in a smart grid. The main challenge lies in the presence of unknown resource transfer functions and cost functions. Our objective is to discover a set of resource allocation solutions that minimise the total cost functions over a specified period. Our approach involves re-constructing a new convex optimisation problem to facilitate the approximation of Q-values in the reinforcement learning training process. Additionally, to avoid continuous communication, we integrate an event-based distributed optimisation approach to approximate the action-value function. We support our methodology with theoretical proofs that establish the validity of the event-triggered conditions and demonstrate the algorithm's convergence. We further validate the algorithm's feasibility through an illustrative example involving output power allocation in a smart grid.

In an increasingly interconnected and data-saturated business environment, organizations are challenged to extract actionable insights from vast and heterogeneous data sources. Big Data integration, when effectively harnessed, offers a transformative pathway for improving operational efficiency and enhancing market responsiveness. The convergence of structured and unstructured data—ranging from transactional records and sensor feeds to social media and customer interactions—requires advanced platforms capable of high-velocity processing and seamless interoperability. Traditional batch-oriented analytical models are being eclipsed by real-time analytics, which enable businesses to monitor, assess, and respond to events as they unfold, significantly reducing latency in decision-making. Real-time analytics embedded within enterprise systems facilitate dynamic process optimization, predictive maintenance, demand forecasting, and rapid customer service interventions. Industries such as manufacturing, logistics, finance, and retail have increasingly turned to real-time data ecosystems to streamline workflows, reduce costs, and preempt operational disruptions. Moreover, by integrating big data with cloud computing, IoT devices, and AI-driven algorithms, businesses can adapt more swiftly to changing consumer behavior, supply chain shifts, and competitive pressures. However, realizing the full potential of big data and real-time analytics entails overcoming significant challenges—including data silos, infrastructure complexity, data privacy, and skill gaps. This paper explores the strategic architecture of big data integration, real-time analytics platforms, and the technologies enabling end-to-end visibility and agility. It further examines case studies where real-time data pipelines have driven measurable gains in performance, offering a roadmap for organizations aspiring to become more intelligent, adaptive, and competitive in fast-moving markets.

Smart homes or the homes of the future will be equipped with advanced technologies for user comfort and entertainment. Intelligent systems will be available to ensure this comfort and reliability. With these technological advancements comes further energy management. The concept of domestic energy efficiency is a concern at present and will be, in the future. So how do we optimize homes and users as to how they conserve energy? Domestic user's energy usage represents a large amount of total electricity demand. Typical home energy systems utilize a rudimentary form of energy efficiency and management. In this paper we look at a Demand Response and Demand side management system model to curb this situation. The demand response system is achieved by the utility turning on/off smart power plugs wirelessly throughout the home based on peak and off peak periods via communication through its smart grid. To help consumers shift their loads during these times, appliance power sources that can act autonomously based on wired or wireless signals received from the utility via its smart grid is required. Users in response to this, connect their appliances to these plugs by generating their own hierarchy system by prioritizing their appliance usage. Whereas the demand side management system allows users to manually configure dates and times for the turning on/off of the smart power plugs wirelessly through the user's smart user interface. Therefore, an energy efficient future smart home that can save the user on monthly expenditure and save on energy simultaneously.

The Energy industry is facing a set of changes. The old grids need to be replaced, alternative energy market is increasing and consumers want more control of their consumption. On the other hand, the ever-increasing pervasiveness of technology together with the smart paradigm, are becoming the reference point of anyone involved in innovation, and energy management issues. In this context, the information that can potentially be made available by technological innovation is obvious. Nevertheless, in order to turn it into better and more efficient decisions, it is necessary to keep in mind three sets of issues: those related to the management of generated data streams , those related to the quality of the data and finally those related to their usability for human decision-maker. In smart grid , large amounts of and various types of data, such as device status data, electricity consumption data, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data . There are several techniques, based on Big Data analysis using computational intelligence techniques, to optimize power generation and operation in real time, to predict electricity demand and electricity consumption and to develop dynamic pricing mechanisms. The aim of the chapter is to critically analyze the way Big Data is utilized in the field of Energy Management in Smart Grid addressing problems and discussing the important trends.

The Smart Grid (SG) is one of the most ultimate topics within the energy communities. It refers to the modernized electric grid, including innovative Information and Communication Technologies (ICTs) to support enhanced power management, protection, security, reliability, and improved costumer choice. To that, SG systems depends on reliable and scalable networking solution to allow efficient communications amongst applications, as power monitoring, protection, demand response and advanced metering, and Intelligent Electronic Devices (IEDs) that provide SG system measurements. However, the deployment of SG applications is complex and very challenging, given the missing of standards and convergent technologies to enable the jointly operation of the underlying heterogeneous systems. SG applications must implement different interfaces to intercommunicate with IEDs in their platforms, process-bus communication protocol, network technologies, and other, limiting the system scalability and time response. We propose the Session Aware Multicast based Architecture (SAMbA) to decouple the intercommunication amongst SG applications and multiple heterogeneous IEDs. SAMbA also leverages multihoming control to keep best connected sessions, and adopts multicast-based channels to achieve optimized communications, while hiding network aspects, as naming and addressing, of IEDs. Simulation results from scenarios under Advanced Metering Infrastructure (AMI) application traffic show that the SAMbA architecture outperforms the typical communication scheme in terms of network performance aspects.

The smart grid (SG) allows integration of renewable energy sources, distributed generation (DG) and storage systems. This chapter builds on the concepts of data management and analytics in SG to build the foundation needed for data analytics to transform Big Data for high‐value action. Big data sources in SG generally fall into two main categories; electric utility data sources and supplementary data sources. The Big Data system will store, process, and mine information in an efficient manner to enhance different SG services. The chapter presents a list of the most common big data tools utilized by successful analytics developers to store, manage, and analyze big data. Big Data needs additional convincing methods to handle the massive amount of information in a limited time span. Any data exchange in SG must be effectively protected through specific “Privacy Concerns” which have potential privacy impacts of SG and smart meter systems.

The smart grid idea was implemented as a modern interpretation of the traditional power grid to find out the most efficient way to combine renewable energy and storage technologies. Throughout this way, big data and the Internet always provide a revolutionary solution for ensuring that electrical energy linked intelligent grid, also known as the energy Internet. The blockchain has some significant features, making it an applicable technology for smart grid standards to solve the security issues and trust challenges. This study will present a rigorous review of blockchain implementations with the cyber security perception and energy data protections in smart grids. As a result, we describe the major security issues of smart grid scenarios that big data and blockchain can solve. Then, we identify a variety of recent blockchain‐based research works published in various literature and discuss security concerns on smart grid systems. We also discuss numerous similar practical designs, experiments, and items that have recently been developed. Finally, we go through some of the most important research problems and possible directions for using blockchain to address smart grid security concerns.

With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.

Abstract: As urban populations grow and cities face increasing challenges in traffic congestion, energy management, and environmental sustainability, Quantum Artificial Intelligence (Quantum AI) is emerging as a transformative technology to optimize urban systems. This book, "Quantum AI for Smart Cities: Optimizing Traffic and Urban Sustainability," explores how the convergence of quantum computing and AI-driven analytics can revolutionize traffic flow management, smart grids, and sustainable urban planning. It delves into quantum-enhanced predictive modeling, real-time optimization algorithms, and autonomous mobility solutions, enabling cities to reduce congestion, lower emissions, and improve resource allocation. The book also examines Quantum AI’s role in energy-efficient infrastructure, climate adaptation strategies, and smart governance models, demonstrating its potential to create data-driven, intelligent, and eco-friendly urban environments. Additionally, it addresses ethical, security, and regulatory considerations, providing insights into quantum cybersecurity, AI governance frameworks, and policy recommendations for responsible adoption. Designed for urban planners, AI researchers, policymakers, and technology leaders, this book serves as a comprehensive guide to leveraging Quantum AI for the next-generation evolution of smart, sustainable, and efficient cities. Keywords: Quantum AI, smart cities, traffic optimization, urban sustainability, quantum computing, artificial intelligence, machine learning, predictive analytics, smart mobility, autonomous vehicles, intelligent transportation, energy management, smart grids, renewable energy, climate adaptation, IoT, big data analytics, sustainable urban planning, ethical AI, AI governance, cybersecurity, quantum encryption, urban infrastructure, smart governance, environmental sustainability, digital transformation.