Energy Management In Smart Cities Research Articles

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures.

An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems.

Digitisation and analysis of energy data at municipal scale: an application to the municipality of Mendrisio

The concept of Smart Cities has gained significant attention in recent years due to the increasing need for efficient energy management in urban areas. This paper describes a digital platform for the management of energy databases that aims to synchronize, update, and analyze different energy installations and uses in the municipality of Mendrisio 1according to the Smart City concept. The platform interweaves and compares various energy databases to ensure better efficiency in the use of thermal and electrical energy, as well as optimize the consumption and production of renewable energies. The geodigital platform created is capable of conducting energy assessments for the development of new district heating projects in the municipality and the creation of local energy communities. This paper presents the design and functionalities of the proposed digital platform, highlighting its potential for improving energy management in Smart Cities and facilitating the transition to more sustainable energy practices. The results of this study, financed by Swiss Energy together with Canton Ticino and AIM (Aziende Industriali Mendrisio), contribute to the growing body of research on digital technologies for energy management in urban areas and provide insights for policymakers and urban planners seeking innovative solutions for sustainable energy development.

Optimal energy management applying load elasticity integrating renewable resources

Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with DSM for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Whale Optimization (WO), and Zebra Optimization (ZO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart.

Digital Forensics for E-IoT Devices in Smart Cities

With the global expansion of urban infrastructure and development of 5G communication technology, advanced information and communications technology has been applied to power systems and the use of smart grids has increased. Smart grid systems collect energy data using Internet-of-Things (IoT) devices, such as data concentrator units (DCUs) and smart meters, to effectively manage energy. Services and functions for energy management are being incorporated into home IoT devices. In this paper, the IoT for energy management in smart cities and smart homes is referred to as the E-IoT. Systems that use the E-IoT can efficiently manage data, but they present many potential security threats, because the E-IoT devices in such homes and enterprises are networked for energy management. Therefore, in this study, to identify vulnerabilities in the E-IoT device systems, digital forensics is applied to the E-IoT device systems. E-IoT devices supplied to Korean power systems were used to build a digital forensic test bed similar to actual E-IoT environments. For digital forensics application, E-IoT data acquisition and analysis methodology was proposed. The proposed methodology consisted of three methods—network packet data analysis, hardware interface analysis, and mobile device paired with E-IoT—which were applied to a DCU, smart meter, smart plug, smart heat controller, smart microwave, and smart monitoring system. On analyzing the user and system data acquired, artifacts such as the device name and energy consumption were derived. User accounts and passwords and energy-usage logs were obtained, indicating the possibility of leakage of personal information and the vulnerabilities of E-IoT devices.

Technological advancements toward smart energy management in smart cities

This comprehensive review paper examines the technological advancements towards smart energy management in smart cities. It provides an overview of the concept of smart energy management, the challenges faced by cities in managing their energy consumption, and the need for technological advancements to overcome these challenges. The advancements are categorized based on their applications, such as smart grids, smart buildings, and smart transportation, and their benefits are discussed, including increased efficiency, reduced costs, and better sustainability. The paper also presents case studies of successful implementation of smart energy management technologies and discusses the challenges faced during implementation and how they were overcome. In addition, the paper highlights potential research areas and emerging technologies, including block chain, edge computing, IoT, big data analytics, energy harvesting technologies, machine learning, and distributed energy resources (DERs). The importance of technological advancements for smart energy management in smart cities is emphasized, and recommendations for future research and development in the field are provided. Overall, this review paper contributes to the ongoing development of smart cities and provides valuable insights for researchers, industry professionals, and policymakers working towards a more sustainable future.

Smart city energy management with IoT renewable system for pitch angle estimation through load shedding frequency modulation scheme for inertia control

Smart city energy management with IoT renewable system for pitch angle estimation through load shedding frequency modulation scheme for inertia control

Privacy-preserving data analytics for smart decision-making energy systems in sustainable smart community

Nowadays, smart community becomes as a key component of Internet of Things (IoT). The fast booming of it significantly enhances the application of smart cities. To achieve data analytics and decision making for energy systems in smart cities, we propose use fog computing to establish a smart community due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging in this scheme because continuous attacks put sensitive information under great threats. Besides, the loss of sensitive information degrade the quality of data analytics and decision making. Motivated by this, we propose a differentially private smart community model with game theory-based personalized privacy protection (GPDP). Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Then we use game theory to model the confrontation and further derive optimized trade-off, which is denoted by the Nash Equilibrium. In addition, we develop a modified reinforcement learning algorithm to achieve fast convergence. The advanced evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility, which improves data analytics and decision-making performances for energy management in smart cities.

Smart building energy management and monitoring system based on artificial intelligence in smart city

In the present scenario, the fastest-growing environmental concerns are energy management and monitoring. In-efficient energy recycling, energy consumption, energy utilization, and drain characteristic are smart building energy management challenges. Hence to examine the connection between smart city management policies and energy management, this research proposed an Artificial Intelligence Technique for Monitoring Systems in Smart Buildings (AIMS-SB) to manage energy consumption and produce and recycle energy required for a smart building. AIMS-SB helps to predict energy analysis, renewable energy production, and recycling evaluation based on prediction model strategies. AIMS-SB developed eco-design monitoring systems for smart buildings to optimize energy consumption, utilization, and drain characteristics. These efficient implementation strategies and methods for harnessing renewable energy help to improve the safety process, recycling, and reuse of our energy resources for smart building energy management. AIMS-SB provides viable solutions to the growing number of challenges associated with smart city energy management. Therefore, the system's findings demonstrate increased accuracy and efficiency compared to conventional methods.

A priority-based seven-layer strategy for energy management cooperation in a smart city integrated green technology

The notion of smart city is based on using both bidirectional power and data flows. For that, designing a persuasive control model for power flow becomes a big and crucial part in a smart city to optimize the power balance between production and consumption. So, a seven-layer smart city energy management strategy (SLSCEMS) for multiple home energy cooperation is presented in this paper. The optimised aims of this strategy are smoothing the smart homes power demand profiles, reducing the electricity bills (E.B), and gaining a total free charging of Plug-in Electric Vehicles (PEVs). This approach has been designed as hierarchic local and global layers. The first one is divided into three layers that aim to transfer the energy between each smart home and its own Renewable Energy sources (RES) and PEVs. The second one is split into four layers that aim to transfer the energy between each smart home and its PEVs, the neighboring homes and their RES, and the smart grid. All optimised layers are based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy is evaluated in a city that contains one hundred homes classified into five categories, each category is designated by its power profile and its flexible number of RESs and PEVs. Simulation results show a decrease in the daily E.B of 26.24%, 2.42%, 60.33%, 29.51%, and 2.38% respectively of the five categories. So, these numerical results prove that the proposed SLSCEMS has considerable efficiency.

IoT-Architecture for Energy Management in Smart Cities

IoT-Architecture for Energy Management in Smart Cities

A Novel Stochastic Blockchain-Based Energy Management in Smart Cities Using V2S and V2G

This paper proposes a secure management framework for optimal scheduling and operation of energy systems in smart cities, which will bring balance in modern power systems. The expanded infiltration of energy systems along with stochastic performance and high data transfer rate can bring new challenges that may severely affect precise energy management. This research work investigates the optimal and secure operation of energy systems in the presence of smart transportation systems in a correlated energy system. To achieve the maximum efficiency from the transportation system, the ideas of vehicle-to-subway (V2S) and vehicle-to-grid (V2G) are utilized to develop a bilateral power flow strategy (charging/discharging or idle mode) to provide the chief charging requirements of energy systems. In this regard, the proposed model suggests a novel stochastic architecture based on unscented transformation (UT) to handle the operational uncertainties of the transportation systems when considering the correlated power generation of wind turbines (WTs). Moreover, blockchain technology is deployed to guarantee the security of data transfer within the smart city. Hence, there is a secure collaboration within all sub-systems in the smart cities, which has triggered the integrated management in this structure. The simulation results are discussed on a typical smart city test system to show the quality of the proposed model.

IoT architecture for energy management in smart cities

IoT architecture for energy management in smart cities

Artificial Jellyfish Optimization with Deep-Learning-Driven Decision Support System for Energy Management in Smart Cities

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.

Editorial

I am very pleased to announce that we have a new editorial structure for the journal, as I had noted in my column in our last issue in 2020. Consequently, since November 2020, seven experienced editors have joined the journal as Senior Editors in order to distribute the papers in their areas of expertise to Associate Editors, and monitor the review process in a more focused manner. Moving forward, authors can now consult a list of keywords representing the expertise of each Senior Editor at the time of submission in order to select the one whose expertise best aligns with their paper. I am grateful to these Senior Editors for their dedication to the IEEE SYSTEMS JOURNAL and for their commitment to elevate the journal's standing in the systems engineering community. This current issue spans diverse topics in complex systems and system-of-systems engineering, and, as always, it also includes many interdisciplinary papers. These topics include complex product manufacturing (as illustrated on the cover page), collaborative resource sharing, vehicular ad hoc networks, fault detection in different types of power grids, energy management in smart cities and smart distribution systems, road safety improvement, radar sensor networks, digital data markets, body area networks, and many others as well. There are also several papers on the application of machine learning to various types Digital Object Identifier 10.1109/JSYST.2021.3059465 of complex systems such as mobile networks, IoT-enabled systems, and wireless charging systems. I am certain that some of these articles will initiate further research in related topics, an established trend that we are now familiar with in the IEEE SYSTEMS JOURNAL.

IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm

Nowadays, the concept of smart cities evolves from conceptual models to various stages of development. The smart services of cities like smart buildings, smart hospitals, smart traffic, smart factories, and smart transportation are vital to energizing resilient, reliable, seamless energy and electrical flow. One of these smart services' critical challenges is anticipated to run without disruptions using smart energy and smart electrical grids. Hence, in this research, the Internet of Things (IoT) role in green energy resources incorporation into smart electrical grids has been introduced using the Multi-Objective Distributed Dispatching algorithm (MODDA). Efficient energy management includes a tradeoff between the cost linked with utility function and energy consumption. The utility function can be the living comfort of the consumers or the utility firm's gross income. The interchanges between utility and energy consumption must, therefore, be recognized. The algorithm is intended to transmit the renewable green energy available to the battery, thermal, and load storing while preserving a thermal and battery storage limit. The experimental results at the lab-scale show the proposed dispatching algorithm minimizing the system cost and energy consumption. It is shown that substantial energy waste can be reduced by equipping a smart building with the proposed IoT system for effective green energy management.

Energy aware smart city management system using data analytics and Internet of Things

The rise of Internet of Things (IoT) concept founded the realization of a smart city. Energy management has lately turn out to be a vital concern for the services of a smart city as IoT devices consume massive energy constantly. This concern needs to be addressed to devise a practical method. Efficient energy utilization aims to promise smart city sustainability. Moreover, IoT devices produce enormous data that is required to be processed efficiently. In this article, a framework is proposed that assures the energy efficiency of IoT devices along with data analysis for cities. This article proposes a general design for smart city energy management that assures the energy efficiency of IoT devices along with data analysis. The proposed model includes three different components that are energy management, data processing, and service management. The energy management component is dependent on infrastructure optimization. The energy-efficient clustering, peak load shaving, optimized scheduling, and load balancing algorithms are integrated to achieve efficient energy management. The data processing is performed using distributed framework. The service management is performed using rules and thresholds. The experiments are performed using authentic datasets and the result highlights the efficiency of the proposed model.

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

With the advent of advancements in the power sector, various new methods have been devised to meet modern society’s electricity needs. To cope with these large sets of electronic device’s current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are two-fold; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly, at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.

Synthetic residential load models for smart city energy management simulations

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterise the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.

An intelligent system for energy management in smart cities based on big data and ontology

PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.