Smart City Energy Research Articles

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In the realm of India's smart cities, precise meter readings are essential for effectively managing domestic energy and water systems. Nevertheless, meter reading employing conventional techniques prove to be both costly and time-consuming, especially considering the vast user base and deficit of everyday consumption analysis. To cope with this challenge, the proposed solution introduces a novel unified wireless smart metering system for measuring energy and water usage. This prototype harnesses the smart, advanced metering technology to manage energy and water resources. The presence of data and remote control capabilities in smart distribution transformers offers distribution operators the chance to optimize system operation and control. The innovative system proposed utilizes smart metering technology and incorporates a distribution system self-healing mechanism against power outages, revolutionizing the way utilities manage water and energy consumption. The system not only offers real-time consumption statistics to the utility provider but also provides the flexibility to remotely control the system's turn-on and turn-off functions. The system effectively captures real-time data, transmits it via LoRa to Telegram application. The system automatically re-establishes remote connections in the event of disconnections caused by emergency conditions or non-payment of the bill, once the relevant issue has been resolved. Additionally, the system promptly notifies authorities on overload, over-temperature, and instances of electricity theft. The system proficiently generates bills based on the consumed data and seamlessly transmits the consumed units to the authorities. The innovative system proposed has proven its ability to communicate effectively, yielding results that align with the given circumstances.

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols.

SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings

With increasing complexity of smart city energy systems and rising energy demand, effective energy management solutions are crucial. Buildings now incorporate renewable energy sources and battery storage for efficient energy utilization, making optimal control strategies important. Compared to rule-based controllers and model-based methods, swarm and evolutionary algorithms have the advantages of providing cost-effective, stable, and scalable alternatives. However, their potential in data-rich environments and multi-building energy systems remains underexplored. This research bridges this gap by demonstrating the cooperative capabilities of population-based optimization agents for efficient energy management. Specifically, novel algorithm and framework, named Swarm Optimized Agents for Sequential Decision Making (SWOAM) is proposed. It combines an online k-means classifier and a swarm optimizer for optimal control of multi-building energy systems. By designing an online K-means learning strategy and an agent-based sequential episodic sampling approach, it is feasible to train a swarm of agents find an optimal energy management policy for buildings in a district. For training and evaluation, we use the standardized CityLearn building energy management environment. The agents are evaluated on three key metrics: electricity cost, carbon dioxide emissions and grid ramping. SWOAM delivers state-of-the-art performance and outperforms modern reinforcement learning and rule-based controllers.

Energy efficiency security in urban areas: Challenges and implementation

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.

The influence of smart city policy on urban green energy efficiency -- A quasi-natural experiment based on 196 cities

Based on 196 prefecture-level cities in China from 2006 to 2021, this paper constructs a weight matrix of urban economic geography and uses the spatial differential model to verify the impact of smart city policies on urban green energy efficiency. The results show that the efficiency value of green energy fluctuates in Chinese cities. Different test results show that smart city policies can effectively improve urban green energy efficiency, but mainly from the time effect rather than the individual effect. It means the improvement in urban green energy efficiency comes more from the fast-growing of the Chinese economy and urban construction in this period than from the country's smart city pilot policy. In addition, from the spatial spillover effect result, China needs to design a better urban spatial development plan to improve the capacity of smart city energy services and vigorously develop intelligent technology represented by information and communication science.

A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

Retracted: The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Retracted: The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.

Strategic Integration of Wind Energy in Smart Cities: Complementing Hydropower Capabilities

Strategic Integration of Wind Energy in Smart Cities: Complementing Hydropower Capabilities

Framework of Data-Driven Methods to Enhance Renewable Energy in Smart Cities

Framework of Data-Driven Methods to Enhance Renewable Energy in Smart Cities

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

Short Review of Solar Energy Markets and Policies for Smart Cities Development

This paper presents a meticulous review of the solar energy markets and their instrumental role in driving the growth of smart cities. Drawing from extensive research, the review delves into urban metamorphoses spurred by the burgeoning growth of megacities, highlighting the significance of sociotechnical systems, evolved institutional practices, and energy material flows. The study identifies seven pivotal models for smart city energy governance, encompassing distributed energy resources, energy storage, microgrids, demand response, smart measuring systems, energy harvesting, and green technological innovations. A comparative analysis of urban developments in cities such as Singapore, Shanghai, and the greater Jakarta area reveals diverse trajectories in energy system evolution and business model shifts. The surging growth of solar energy, underpinned by technological breakthroughs, cost efficiencies, and robust governmental policies, is also explored. Although solar energy has been bolstered by a myriad of fiscal and regulatory incentives, it faces persistent challenges like technical impediments, financial barriers, and regulatory obstacles. The paper also introduces an innovative compound Real Options Approach (ROA) for appraising solar energy projects within smart cities, arguing its efficacy over the traditional Expected Net Present Value methods by recognizing managerial adaptability. In summation, this review accentuates the imperative of enduring policy support to amplify solar energy adoption worldwide, setting the stage for a sustainable smart city future.

Study on the impact of smart energy on carbon emissions in smart cities from single and holistic perspectives – Empirical evidence from China

Smart energy, as an important component of smart cities and directly related to carbon emissions, plays a crucial role in smart cities to achieve carbon neutrality, but the theoretical research and empirical evidence on the impact of smart energy on carbon emissions in smart cities are limited. This paper investigates, from single and holistic perspectives, the effect of smart energy on smart city carbon emissions and potential mechanisms. Based on the panel data of 100 smart cities in China from 2014–2019, it uses synthetic difference-in-difference mode and spatial difference-in-difference mode to estimate the carbon emission reduction effect and test the potential mechanisms. The results show that: (1) Smart energy reduces carbon emissions in smart cities by an average of 5.65 %. (2) Smart energy reduces smart city carbon emissions mainly through mechanisms including optimizing energy structure and improving energy efficiency. (3) Smart energy in smart cities has a significant spatial carbon emission reduction effect. (4) Smart energy in smart cities influences holistic regional carbon emissions through spatial mechanisms including technology spillover, peer effect, and scale effect. Finally, this study provides quantitative evidence and valuable insights for cities in developing countries with similar backgrounds to China to design emission reduction paths and adjust smart city strategies.

Retracted: Attribute Reduction Algorithm on Concept Lattice and Application in Smart City Energy Consumption Analysis

Retracted: Attribute Reduction Algorithm on Concept Lattice and Application in Smart City Energy Consumption Analysis

Analysis and hypothesis testing of redundant energy of solar home systems without net-metering

Solar home systems (SHS) are increasing being deployed as sustainable energy supply for the residential sector to meet the Sustainable Development Goal 7 target by 2030, especially for countries in sub-Saharan Africa (SSA) where national grid electricity supply is inadequate or weak. For SHS in SSA, however, a unique challenge exists as many of the households do not have access to net-metering system that allows extra PV energy generation during the daytime to be exported to the grid. This leads to waste energy generation (redundant energy) when the household energy demand is lower than the PV energy generation. In this study, analysis has been conducted to determine the magnitude of redundant energy of 3 SHS. Hypothesis testing of the existence of redundant energy from the SHS is also conducted. Our study has revealed that generally, there is redundant energy generation in the hours of 10 a.m. to 3 p.m. for the households, with hourly values ranging from 0.37 kWh to 1.55 kWh. The redundant energy represents 29.6 %–56.3 % of the households' monthly PV energy generation. The findings of this study give insights into the potential of harnessing redundant energy of SHS for planning smart energy cities if net-metering systems were available.

Selection of optimum green energy source under smart city environment for sustainable energy management and planning: An eclectic decision

The purpose of this paper is to present a decision support framework, able to assess and optimize the energy use in smart cities. This paper has taken a sincere endeavour to develop an innovative integrated analytical framework as a benchmark to understand to what extent the energy has been consumed in smart city spectrum. Initially five sectors (construction, water management, transport, waste treatment and public services) of smart city and their corresponding sub-factors are identified based on industry, academia partnership. Once the data were gathered, those were analysed using various statistical tools. This research is useful for the policymakers, executive people, especially those are working or involved in smart city development projects. The developed framework helps to identify the significant energy consumption sector and also suggest the suitable green energy alternatives for developing a cleaner and sustainable future. As this study discusses the various parameters related to smart city energy consumption sector in western India, it will have a huge practical potential on the proposed operational smart cities in India. Literature has witnessed minimum number of studies have been carried out on this proposed framework and that could improve the wellbeing of the people living in the cities.

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements.

Reliability-Oriented Planning Framework for Smart Cities: From Interconnected Micro Energy Hubs to Macro Energy Hub Scale

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

Municipal-Based Biowaste Conversion for Developing and Promoting Renewable Energy in Smart Cities

In the current scenario in many cities, huge quantities of biowaste solid matter are generated, making it a big challenge to keep our cities smart/clean without creating health issues. The second challenge is to mitigate solid biowastes from municipalities, and it needs systematic valorization/conversion approaches to transform/generate them into clean/least carbon-emitting fuel sources. This effort can help make smart cities with additional amounts of energy generation that can be used by each citizen for their daily energy needs. In the cities, biowastes are reported as food waste (from domestic kitchens and restaurant/hotel), green plant residues (from parks and other cleaning activities), and other miscellaneous sources. Due to the huge generation of these biowastes, the respective cities can look dirtier and also show a poor level of development. So, people from municipal authorities and some research groups have to start converting this biowaste solid matter into renewable and sustainable energy that can help reduce this biowaste accumulation through the promotion of sustainable bioenergy sources. In valorization approaches, biological (anaerobic digestion) and thermochemical (like pyrolysis) processes are common, and these can be applied to biowaste mitigation to minimize the negative impact on the environment and its components. In India, some states, like Madhya Pradesh, have put efforts into creating biogases like green hydrogen from their cities generated biowastes. In this review, we emphasize the different sources of biowaste in cities with their volumes/quantities, factors/activities for generations, and mitigation approaches for biowaste conversion into fuels with the promotion of sustainable goal achievement. Now that waste matters, effort is put into a take-home concept/message regarding the conversion/recovery, and extraction of waste into energy/other valuable products for home needs. It helps keep cities clean, smart, and rich in revenue sources.

Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities

To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessary system scope simplifications. This has resulted in disconnected system scales, namely, building occupants (bottom layer) and smart-city energy networks (top layer). This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning. To assess the aggregated impact of user behavior stochasticities on optimal urban energy planning, a stochastic energy community sizing and operation problem is designed, encompassing multi-level utilities founded on energy hub concepts for improved energy and carbon emission efficiencies. The problem is solved through an organic spatial problem distribution suitable for field deployment, validated by a proof of concept. We examine uncertainty factors affecting urban energy planning through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on this modeling setup, an energy community of 41 Dutch residential buildings is optimally designed using historical measurements. Results disclose a fast-converging distributed stochastic problem, showcasing boilers as the preferred heating utility. Distributed renewable energy and storage systems are identified as unprofitable for the community. Occupant behavior is particularly exposed as the leading uncertainty factor impacting energy community planning. This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies.