IoT-Architecture for Energy Management in Smart Cities

5.12 Energy Management in Smart Cities

Energy is an essential element for human activities across the globe, and the wind turbine is a device that produces renewable electrical power for green energy management. Recently, for the establishment of smart green cities, the large-scale horizontal axis wind turbine (HAWT) installations have received greater attention. The objective of the present chapter is to provide deep insights on the application and feasibility of novel wind turbine configurations to accomplish energy management in green smart cities. The basic design and construction of such wind turbines are modified to generate power even at low wind speeds (i.e., < 5 m/s), that is attainable adjacent to shallow waters in urban environments. The optimum parameters such as the tower height, number of rotor blades, rotor blade design (aerofoil shape) and control systems are determined through the industry standard Bladed module. The NACA profile is considered to design the untwisted/twisted blades for wind turbines to deliver maximum power output at low wind speeds with hybrid composite epoxy blades. Detailed numerical simulations are provided with fixed and variable pitch blades at different possible wind speeds prevailing in the coastal environments of Europe and India. In France, in the Normandy region alone, the offshore wind power projects with about 500 MW capacity are installed, which satisfies the electricity requirements of about 770,000 people annually. The turbine operation and control will be done by artificial intelligence (AI) based techniques. The power output range varies from 1.5-2 MW, which depends on various geometric and environmental constraints, and it helps to fulfil the domestic power requirement of smart cities in a cost-effective manner to achieve the green energy mission.

Rapid urbanization and climate change demands require transformative strategies for energy management in smart cities. Traditional computational techniques encounter substantial difficulties in optimizing intricate, dynamic, and large-scale energy resource allocation issues characteristic of contemporary smart grids. This conceptual review examines the emerging paradigm of quantum-enhanced resource allocation strategies for sustainable energy management in smart cities. This paper expands on the foundational framework of quantum computing applications for smart grid digital twins proposed by Lemo et al., synthesizing current research, developing an integrated conceptual model, and identifying critical research trajectories. We systematically analyze how quantum algorithms—specifically Quantum Annealing (QA), the Quantum Approximate Optimization Algorithm (QAOA), and the Variational Quantum Eigensolver (VQE)—can tackle significant optimization challenges in energy distribution, demand-response management, and renewable energy integration. Our analysis highlights the intersection of quantum computing and digital twin technology as a crucial facilitator for real-time, adaptive energy management systems. We propose a five-layer Quantum-Digital Twin (Q-DT) integration framework and examine its implications for sustainable urban development. The paper concludes by identifying critical research deficiencies, such as the necessity for standardized problem mapping, empirical benchmarking studies, and ethical governance frameworks, while delineating a prospective research agenda for achieving quantum advantage in smart city energy ecosystems. Keywords: Quantum Computing, Resource Allocation, Intelligent Urban Environments, Sustainable Energy Management, Digital Twins, Quantum Optimization, Smart Grid, Integration of Quantum and Digital Twins Aims Research Journal Reference Format: Ojeniyi, J. A., Fasola, O. O., Onyeabor, G. A., Mohammed, A. A, Aliyu, A. A., Ahmed, H. M. & Ndanusah, U. H. (2026): A Conceptual Review of Quantum-Enhanced Resource Allocation Strategies for Sustainable Energy Management in Smart Cities. Advances in Multidisciplinary Research Journal. Vol. 12 No. 1, Pp 55-61. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V12N1P4

Energy management in smart cities is a critical challenge due to the increasing population, urbanization, and growing energy demand. Efficient energy forecasting mechanisms are vital to optimize consumption, enhance sustainability, and ensure a balanced energy supply. This paper presents an energy consumption forecasting approach tailored for smart cities, leveraging advanced predictive analysis techniques. By employing machine learning models, the system forecasts energy consumption patterns based on historical data, real-time data streams, and environmental factors. The aim is to help ur-ban authorities and policymakers manage energy resources more effectively while improving energy efficiency in smart city infrastructures. This paper investigates the accuracy and performance of two predictive models for en-ergy forecasting: a Support Vector Regression (SVR) model and an Artificial Neural Network (ANN). The study compares the performance of these models in terms of forecast accuracy, computational efficiency, and adaptability to real-time data. Extensive testing is performed on simulated datasets to assess the models under different environmental conditions. Finally, the paper dis-cusses the implications of these models for energy management and decision-making in smart cities.

As a promising future Internet architecture, named data networking (NDN) supports namebased routing and caching for content retrieval throughout the network, which enables fast, reliable, and, more importantly, energy-efficient content dissemination in smart cities. However, NDN's vulnerability against the content poisoning attack is considered to decelerate the process of applying NDN to energy management in smart cities. The content poisoning isolates valid content from the network by injecting a poisoned content with a legitimate name into in-network caches. The caching, delivery, and signature verification of poisoned content diminish the advantage of NDN in energy efficiency when it disseminates content in smart cities. This paper focuses on content poisoning mitigation for energy management in smart cities and first analyzes the state of the art and the challenges of content poisoning mitigation. We then propose a light-weight mitigation mechanism by enhancing NDN with a name-key-based forwarding and multipath forwarding-based inband probe. Name-key-based forwarding forwards interests toward content sources trusted by consumers to reduce the injection of poisoned content. If there is still on-path content poisoning, the multipath forwarding of a reissued interest that excludes poisoned content acts as inband probes and invokes on-demand signature verifications at intermediate routers. This purges poisoned content from caches as soon as possible, delivers legitimate content to the present consumer, and restores legitimate content retrieval for future Interests without requiring any out-of-band communications. Our experimental results demonstrate that our proposed content poisoning mitigation mechanism restores legitimate content retrieval pretty soon with relatively small verification overhead at intermediate routers and is well adapted to diverse network settings, which would accelerate the deployment of NDN in smart cities to disseminate content in an energy-efficient way.

Technological advancements toward smart energy management in smart cities

Energy management in smart cities has gained particular significance in the context of climate change and the evolving geopolitical landscape. It has become a key element of sustainable urban development. In this context, energy management plays a central role in facilitating the growth of smart and sustainable cities. The aim of this article is to analyse existing scientific research related to energy in smart cities, identify technological trends, and highlight prospective directions for future studies in this field. The research involves a literature review based on the analysis of articles from the Scopus and Web of Science databases to identify and evaluate studies concerning energy in smart cities. The findings suggest that future research should focus on the development of smart energy grids, energy storage, the integration of renewable energy sources, as well as innovative technologies (e.g., Internet of Things, 5G/6G, artificial intelligence, blockchain, digital twins). This article emphasises the significance of technologies that can enhance energy efficiency in cities, contributing to their sustainable development. The recommended practical and policy directions highlight the development of smart grids as a cornerstone for adaptive energy management and the integration of renewable energy sources, underpinned by regulations encouraging collaboration between operators and consumers. Municipal policies should prioritise the adoption of advanced technologies, such as the IoT, AI, blockchain, digital twins, and energy storage systems, to improve forecasting and resource efficiency. Investments in zero-emission buildings, renewable-powered public transport, and green infrastructure are essential for enhancing energy efficiency and reducing emissions. Furthermore, community engagement and awareness campaigns should form an integral part of promoting sustainable energy practices aligned with broader development objectives.

Due to the ongoing urbanization trend, smart cities are critical to designing a sustainable future. Urban sustainability involves action-oriented approaches for optimizing resource usage, ecological impact reduction, and overall efficiency enhancement. Energy management is one of the main concerns in urban, residential, and building planning. Artificial Intelligence (AI) uses data analytics and machine learning to instigate business automation and deal with intelligent tasks involved in numerous industries. Thus, AI needs to be considered in the strategic plan, especially in the long-term strategy of smart city planning. Decision Support Systems (DSS) are integrated with human-machine interaction methods like the Internet of Things (IoT). Along with their growth in size and complexity, the communications of IoT smart devices, industrial equipment, sensors, and mobile applications present an increasing challenge in meeting Service Level Agreements (SLAs) in diverse cloud data centers and user requests. This challenge would be further compounded if the energy consumption of industrial IoT networks also increased tremendously. Thus, DSS models are necessary for automated decision-making in crucial IoT settings like intelligent industrial systems and smart cities. The present study examines how AI can be integrated into DSS to tackle the intricate difficulties of sustainable energy management in smart cities. The study examines the evolution of DSSs and elucidates how AI enhances their functionalities. The study explores several AI methods, such as machine learning algorithms and predictive analytics, that aid in predicting, optimizing, and making real-time decisions inside urban energy systems. Furthermore, real-world instances from different smart cities highlight the practical applications, benefits, and interdisciplinary collaboration necessary to successfully implement AI-driven DSS in sustainable energy management.

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches.

With the increasing demand for energy in rapidly urbanizing cities, ensuring sustainability while maintaining efficiency has become a critical challenge. Artificial Intelligence (AI) is transforming energy management in smart cities by optimizing energy consumption, integrating renewable sources, and enhancing grid reliability. This review paper explores the role of AI-driven solutions in sustainable energy management, focusing on smart grids, energy-efficient buildings, predictive analytics for demand forecasting, and real-time energy optimization. By analyzing case studies from Indian smart cities, this study identifies existing challenges such as data security, infrastructure limitations, and policy constraints. Furthermore, it highlights the potential of AI in supporting decentralized energy systems, such as microgrids and peer-to-peer energy trading, fostering a more resilient and adaptive urban energy ecosystem. This review aims to provide valuable insights for urban planners, policymakers, and researchers working toward AI-powered sustainable energy solutions in smart cities.

Digital Twin technology has emerged as transformative approach in energy management in smart cities, large facilities, offering real-time monitoring, predictive analytics, adaptive control to enhance efficiency and sustainability. This paper develops multi-layered Digital Twin framework that integrates IoT sensor networks, data acquisition, preprocessing, dynamic virtual modeling, AI-driven analytics, user-centric decision support. The framework is deployed in simulated smart city environment, evaluated through 12-month case study, demonstrating progressive energy consumption reductions from baseline levels to a 65% decrease by end of period. Predictive maintenance models based on machine learning algorithms accurately forecast equipment faults, reducing unplanned downtime, maintenance costs. Renewable energy integration is optimized through scenario testing, balancing supply variability with storage management. The results validate that Digital Twins enable proactive demand response, improve operational resilience, facilitate informed policy decision-making. Key contributions include scalable architecture for city-wide energy management, red dotted polynomial trendline analysis of energy savings over time, comprehensive literature synthesis identifying critical research gaps in data integration, interoperability, cybersecurity. Future work will focus on edge-computing enhancements, blockchain-based data security, standardized interoperability protocols to support cross-domain implementations. This research confirms potential of Digital Twins to drive sustainable, cost-effective urban energy systems.

This paper proposes a service-oriented architecture to support big data analytics for buildings energy management in smart cities. This architecture allows for seamlessly integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations. These operations are needed to effectively utilize available big data for optimizing energy consumption for residential, industrial, and commercial buildings in smart cities. The paper also recognizes the different kinds of decision making processes required by a smart city to effectively manage energy efficiency for its buildings. These decision-making processes can be implemented and deployed as services in the proposed architecture.

Green computing is an Eco-Friendly usage of resources in terms of designing, displaying and manufacturing in the field of Engineering. Main purpose of green computing is reducing the environmental impact such as hazardous materials, energy efficiency during the lifetime of product and recyclability of product wastage. The main approaching fields of green computing are data center design, cloud computing, edge computing, IoT, super Computer and smart cities etc. The one of the main approaches of green computing with smart cites is collect and manage various recourses and use efficiently and effectively. In smart cities data storing, processing and using take more usage of resources are wasted. In Energy management and effectively usage is big challenging issues in recent days in the world. The Green computing with energy efficiency in a smart cities research help to improve the energy effectively. The Energy management in smart cities is tracking and monitoring the energy to use effectively in building and smart cities. In smart build and smart cities around 25–35% cost is spent to energy operations. Energy managing purpose different techniques and methodologies are introduced. Internet of things (IoT) is one of the main techniques to sense and optimize the unrelated events. In this chapter mainly concentrated integration of IoT and CupCarbon and implementation. Specially first, IoT Smart Road Network Energy management using CupCarbon is implemented with the help of Road Side Unit (RSU) and IoT protocol (MQTT). Using this implementation, we can Manage the power in the street light in road network (power optimization), Analysis the traffic in one particular and Emergency services we can take the decision. The second, Case Study with IoT Energy Management with CupCarbon in VANET environment. Finally presented various challenges and research direction for future.

Sustainable Urban Development: A Systematic Literature Review with Bibliometric Analysis on the Evaluation of the Impact of IoT on Sustainable Development and Energy Management in SMART Cities

A city is considered to be smart when the application of Artificial Intelligence (AI) and the Internet of Things (IoT) is integrated with it. This enables the collection of data from people, devices, and buildings, then analyses are performed to optimize control over infrastructure, traffic, energy, etc. A smart city is a collective framework with the integration of Information and Communication Technologies (ICT) and Cloud that makes interaction easily with one another. In this chapter, smart energy infrastructure is studied to monitor energy utilization in the city and to reduce costs and carbon emissions. Energy usage has recently shifted focus to renewable energy sources with minimal carbon emissions, emphasizing the necessity for ongoing environmental and human health preservation. Renewable energy is becoming more abundant, and the issue is to recognize and understand it in meeting the increasing demand for clean, affordable energy. Customers, distributors, and government bodies are all concerned about cost and the climate. Artificial intelligence proclaimed a new age in technology as well as in sustainable development. So, in this chapter, an implication of AI is presented and analyzed for RE research in smart environments. Along with that, an analytical study is also presented with the application of AI or IoT for smart energy management for smart cities. The main aim is to focus on and explore the efficiency level of ML/IoT techniques. This work will also provide an in-depth analysis of innovative development, deployment, analysis, and management of smart energy in smart cities.KeywordsSmart citiesRenewable energySmart gridsArtificial intelligence (AI)Machine learning (ML)Internet of Things (IoT)