City Energy Management Research Articles

Integrating Deep Reinforcement Learning with Evolutionary Algorithms for Advanced Optimization in Smart City Energy Management

Integrating Deep Reinforcement Learning with Evolutionary Algorithms for Advanced Optimization in Smart City Energy Management

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The model’s performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future.

Transforming City Energy Management: Using Smart Grid Technologies for Improved Control and Energy Efficiency

The research focuses on the application of Smart Grid to optimize energy consumption in urban conditions. This work contains practical examples and analysis of research results that demonstrate the effectiveness of using sensor networks and real-time automated systems for monitoring and controlling energy consumption. The study contains practical recommendations for using Smart Grid technologies to improve the sustainability and efficiency of urban infrastructure. Analysis of research results and real examples highlights the significant potential of Smart Grid in ensuring efficient energy management in cities. By introducing Smart Grid technologies, it becomes possible to achieve effective management of energy supply, reduce energy consumption and increase the stability of electricity distribution systems in the urban environment. The results of this research offer practical ideas for improving energy supply and sustainability, while contributing to the implementation of innovative solutions for efficient energy management in cities.

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

Smart Grid Integration for Sustainable City Energy Management: A Paradigm Shift in Power Distribution Networks

This study focuses on the investigation of collective intelligent systems in the context of energy management in modern distribution networks. The main objective of the research is to explore the potential of utilizing collective intelligent systems for achieving sustainable and efficient energy management in cities. The study describes the concept of collective intelligent systems and their role in distribution networks. The research findings emphasize the importance of employing such systems for achieving optimal resource allocation, enhancing energy consumption efficiency, and promoting sustainable urban development. A novel approach to smart energy management based on collective intelligent systems is discussed in the study. The results of the research demonstrate the advantages of this approach, including more accurate and efficient energy management, reduction of losses, and improvement of network stability. This work summarizes the significant research outcomes of collective intelligent systems in the context of energy management. It enhances understanding of the pos-sibilities and benefits of utilizing such systems in distribution networks and encourages further research for advancing energy management in 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.

Computational Models for Forecasting Electric Vehicle Energy Demand

Electric vehicles (EV) are fast becoming an integral part of our evolving society. There is a growing movement in advanced countries to replace gas-driven vehicles with EVs towards cutting down pollution from emissions. When fully integrated into society, electric vehicles will share from energy available on the grid; therefore, it is important to understand consumption profiles for EVs. In this study, some computation models are developed from predicting day-ahead energy consumption for electric vehicles in the city of Barcelona. Five different machine learning algorithms namely support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANN), decision tree (DT), and ensemble learners were used to train the forecasting models. The hyperparameters for each of the ML algorithms were tuned by Bayesian optimization algorithm. In order to propose efficient features for modeling EV demand, two different model structures were investigated, named Type-I and Type-II model. In the instance of the Type-I model, seven regressors representing the consumption of the previous seven days were considered as input features. The Type-II models considered only the EV consumption on the previous day and on the same day in the previous week. Based on the results in this study, we find that the performance of the Type-II models was as good as the Type-I models across all the algorithms considered although less input features were considered. Overall, the all algorithms employed in this study gave about 75-80% model accuracy based on the R 2 performance criterion. The models formulated in this study may prove useful for planning and unit commitment functions in city energy management functions.

Retraction Note: Research on intelligent city energy management based on internet of things

Retraction Note: Research on intelligent city energy management based on internet of things

A sub-system design comparison of renewable energy based multi-generation systems: A key review along with illustrative energetic and exergetic analyses of a geothermal energy based system

In recent years it has gained a significance importance to decrease ecological footprint so to have a positive contribution to environmental quality. Renewable energy plays a significant role in future trends for efficiency and environmental issues. Efficiency of systems is to be increased to meet future demands in terms of sustainability with increased consumption. There is a need for new and innovative research studies on promising technologies, processes and strategies to have sustainable water and energy management in cities. This review paper investigates the use of energy sources in single or multiple forms with different system designs from simple to complex structures. The examined studies were presented in a tabulated form including energy and exergy efficiency values as well as the methods and tools used. The results indicated that the systems with the highest energy and exergy efficiency values had several useful outputs, such as hydrogen, fresh water, drying, heating and cooling etc. The maximum exergetic efficiency was determined to be 94% for a multiple energy sourced multi-generation system while the maximum energetic efficiency was calculated to be 111.3% for a solar assisted and heat pump integrated multi-generation system amongst the reviewed studies. To illustrate the obtained results from review, a geothermal energy based multi-generation system was considered. Some parametric studies were also undertaken to see the effects of geothermal water temperature and flow rates on efficiency values. For a single production case, they were determined to be 13.7% and 50%, respectively while they were obtained to be 98.6% and 67.7% for a multi-generation system, in which electricity, low grade hot water production, high temperature hot water production and hydrogen were useful outputs. However, it should be noted that there was a threshold about the number of products to have an optimum increase in the efficiency values.

Energy management in the public building sector – ISGE/ISEMIC model

This paper introduces Intelligent Information System for Monitoring and Verification of Energy Management in Cities (ISEMIC) web application that connects processes of gathering data on buildings and their energy and water consumption, monitors consumption indicators, detects any anomalies or irregularities in time, sets energy efficiency targets and reports energy and water consumption savings. ISEMIC enables use of smart meters within an energy management for the first time in the region, along with an analytical part which enables intelligent estimation of energy consumption based on multiple criteria. In the public sector are enormous potential for energy and water savings and thus a large area for ISEMIC web application implementation. ISEMIC web application is developed in July, 2011. Purchase of smart metering equipment and establishment of smart metering infrastructure on institutions of project partners responsible for web application development is in conduction after which a full pilot run will be started. The potential impact of this project is very large and it would be a great example how significant savings can be achieved by systematic energy monitoring and management provided by the use of ISEMIC web application.

Blockchain Smart Contract Technology in Urban Energy Management System

Blockchain technology is considered a disruptive technology with real poten-tial for change after steam engines, electricity, and the internet. The regionalelectricity sales model based on blockchain technology will become anessential breakthrough in innovative city energy management. The thrust-less ingenious contract mechanism, the weakly centralized intelligent powerenergy micro-grid internal competitive energy business model, is based onblockchain technology. Its physical structure includes multiple levels suchas the energy use layer, control layer, and functional layer. All transactionentities of the innovative power energy microgrid have an equal status in theblockchain system. The transaction energy includes cold, heat, electricity,gas, etc., and is distributed after bidding through energy routers and com-petitive trading platforms. This model uses multi-party bidding to optimizethe allocation of resources such as cold, heat, and electricity, restores thecommodity and financial attributes of energy and dramatically improves theenergy efficiency and economic benefits of the power energy microgrid.

Markov model in home energy management system

An intelligent home energy management system was proposed. Reinforcement learning and a markov prediction model were used to help the system make decisions. The Markov model predicted the future state of users or the weather, and the intelligent decisionmaking support system sent signals to local controllers to control furniture. This work benefits energy management because if the system knows the user’s next state, it can control a specific appliance to save energy. Meanwhile, if the system can predict the weather, the house can use green energy rationally. The proposed energy management system could be applied in an intelligent house, city energy management systems, and building energy management. The state prediction helped the decision-making system make accurate and rational decisions.

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.

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.

Internet-of-things-based optimal smart city energy management considering shiftable loads and energy storage

Formulating a novel mixed integer linear programing problem, this paper introduces an optimal Internet-of-Things-based Energy Management (EM) framework for general distribution networks in Smart Cities (SCs), in the presence of shiftable loads. The system’s decisions are optimally shared between its two main designed layers; a “core cloud” and the “edge clouds”. The EM of a Microgrid (MG), covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as the input parameters of the core cloud optimisation problem, the DSO optimises the network’s total operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged in low energy prices through the purchasing power from the market and discharged in high energy prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy balance with the minimum cost.

RETRACTED ARTICLE: Research on intelligent city energy management based on Internet of things

In order to solve the problem of energy management in Intelligent City, the energy management platform of the Internet of things is applied. the method of the energy consumption measurement data access to the energy management platform of the Internet of things is studied. According to the different communication protocols used by the energy management subsystem, the access methods of OPC technology, Web Service and open database connectivity and front-end communication are discussed. The results show that the intelligent city energy management system based on the Internet of things can meet the needs of the “intelligent city” construction. The system integrates the isolated energy information and realizes the energy consumption measurement. It lays the foundation for the diagnosis and energy saving evaluation of urban energy consumption.

An efficient city energy management system with secure routing communication using WSN

The major goal of this work is to reduce the economic consumption of power in the electricity as well as reduce the cost even in the high peak time usage of electricity. Another aspect is to maintain the information secure during the transmission between the producer and consumer. Here, need to maintain the balance on the efficient demand by reducing the cost of the electrical power. This proposed approach is developed to minimize the energy consumption, maintain the load in the peak hours and reduce the cost based on the TOU tariff. This helps to enhance the OREM scheme. Simulation results have performed using iHEM technology with the local energy presence. Another important aspect of the proposed approach helps to detect the intrusions. With the help of a modified dynamic source routing with adaptive local routing protocol routing (MDSR-ALR) protocol, misrouting paths are identified in wireless sensor network. These misrouting paths will generate a higher drop rate and that can be detected and reduced by the proposed application. This proposed WSN-based city energy management applications approach uses MDSR-ALR protocol which deals with the collection of information about neighbors, shortest path, and normal path and then guard time. Finally, this proposed protocol concentrates on secure routing as well as reduce the energy consumption and cost and then maintain the load.

기상정보를 활용한 도시규모-EMS용 태양광 발전량 예측모델

기상정보를 활용한 도시규모-EMS용 태양광 발전량 예측모델

Practical Diagnostic Approach of Energy Consumption of AC Systems

Due to the vast amount of energy consumption of AC (Air Conditioning) systems, the reasonable design, optimal operation and efficient management of AC systems are inevitable and necessary to the whole city energy management (CEM). To achieve these objectives, the verification of systematic characteristics (mainly refers to the systematic COP), practical approaches of energy consumption diagnosis of AC systems should be taken as the premises and the most elementary works. Itemized metering of the driving power of each compressor, pump and fan is recognized as a key process needed for optimal operation and maintenance of air conditioning systems. Based on AC commissioning principle, a practical diagnostic approach of energy consumption of AC system is established. The framework and general procedures of the practical approach is presented and introduced in this paper.