Comparison Of Energy Consumption Research Articles

Energy-Efficient Route Planning Method for Ships Based on Level Set.

To reduce the fuel consumption of ships' oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton-Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles.

The Energy Consumption Comparison of Temperature Difference-based Defrosting Exit Method and Time-based Defrosting Exit Method with Various Static Pressure Differences

The time-based defrosting exit method(TDEM) has been widely used in cold storage, but the issue of energy consumption has been heavily criticized. In this paper, a temperature difference-based defrosting exit method(TDDEM) is proposed to compare with TDEM. Firstly, a parameter acquisition system of cold storage is constructed, and the experimental setup is conducted to determine the experimental parameters. Subsequentially, the range of defrosting static pressure difference is determined based on pre-frosting experiment, and the defrosting experiment of TDEM and TDDEM are carried out at five various static pressure difference conditions, 16Pa, 14Pa, 12Pa, 10Pa and 8Pa, respectively. The results show that the temperature fluctuations per defrosting events of TDEM are more severe compared to TDDEM, with with average increases of 102.2%, 91.6%, 59.3%, and 46.5%, respectively. Energy consumptions of TDDEM are markedly lower than TDEM, with reductions of 21.4%, 15.7%, 14.09% and 14.59%, respectively. Whether TDEM or TDDEM, the lowest energy consumption is occurred at 10Pa, and highest is 8Pa.

Advancing Neural Networks: Innovations and Impacts on Energy Consumption

AbstractThe energy efficiency of Artificial Intelligence (AI) systems is a crucial and actual issue that may have an important impact on an ecological, economic and technological level. Spiking Neural Networks (SNNs) are strongly suggested as valid candidates able to overcome Artificial Neural Networks (ANNs) in this specific contest. In this study, the proposal involves the review and comparison of energy consumption of the popular Artificial Neural Network architectures implemented on the CPU and GPU hardware compared with Spiking Neural Networks implemented in specialized memristive hardware and biological neural network human brain. As a result, the energy efficiency of Spiking Neural Networks can be indicated from 5 to 8 orders of magnitude. Some Spiking Neural Networks solutions are proposed including continuous feedback‐driven self‐learning approaches inspired by biological Spiking Neural Networks as well as pure memristive solutions for Spiking Neural Networks.

Optimizing energy efficiency in buildings’ cold water systems: A differential pressure control-based global approach

Buildings’ cold water systems have become increasingly complex, with independent equipment control leading to greater energy consumption and greenhouse gas emissions. To improve control efficiency and maximize energy savings, simulation and experimental studies of these systems are essential, although full-scale experimental setups are rare. This paper employs FloMASTER modeling to establish a large-scale practical pipeline network system for validating models, evaluating strategies, and optimizing energy efficiency. The modeling method is validated as accurate, with normalized mean bias error (NMBE) and coefficient of variation of the root mean square error (CVRMSE) values for nodal pressure and flow distribution meeting established evaluation criteria. A quantitative evaluation of constant differential pressure control strategies, considering hydraulic and thermal characteristics as well as energy consumption, reveals that the strategy based on intermediate loop users performs best. This strategy achieves an average hydraulic imbalance rate of only 5.05%, the quickest hydraulic stabilization and restoration time, and comparable secondary pump energy consumption across three control strategies. Furthermore, this simulation study proposes a global combined control strategy that enables more stable operation of the system, reduces chiller energy consumption by 4.66%, secondary pump energy consumption by 9.93%, and overall system energy consumption by approximately 5.38%. These findings suggest that this methodology can be applied to large-scale pipeline networks in complex cold water systems, yielding substantial energy savings.

Measuring the Effectiveness of the 'Batch Operations' Energy Design Pattern to Mitigate the Carbon Footprint of Communication Peripherals on Mobile Devices.

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named 'Batch Operations' (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.

Comparison of energy consumption and probiotic stability with pilot scale drying processes

The production of dried probiotics offers multiple advantages including expanded storage options, ease of transportation, enhanced stability, formulation support, and incorporation into a variety of finished products. As probiotic use and application areas increase, the demand for lower production costs and increased product stability follows. The most common methods for drying include lyophilization and spray drying. Lyophilization offers high preservation of viability at the expense of production speed, while spray drying provides high throughput drying with greater loss of probiotic viability. Newer processes, such as electrostatic spray drying, have the potential to bridge the gap. The aim of this study was to evaluate the energy consumption and viability of Lacticaseibacillus rhamnosus GG (LGG) with pilot-scale conventional spray drying, electrostatic spray drying, and lyophilization. Energy consumption of the primary utilities was monitored along with LGG viability immediately and 8–12 weeks after drying. With regards to removal of water, conventional spray drying was the most energy efficient (1.2 kWh/L), followed by electrostatic spray drying (10.9 kWh/L) and lyophilization (16.9 kWh/L). When normalized against recovered viable LGG, electrostatic spray drying was the most efficient at 4.6 × 1010 CFU/kWh, followed by lyophilization and conventional spray drying at 1.1 × 1010 CFU/kWh and 4.8 × 107 CFU/kWh, respectively.

Investigating the Heterogeneity Effects of Urban Morphology on Building Energy Consumption from a Spatio-Temporal Perspective Using Old Residential Buildings on a University Campus

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions.

Comparative Study of Energy Consumption Intensity: A Case Study of the Faculty of Electrical Engineering and Informatics (FEI) Versus the Faculty of Chemical Technology (FChT) in the City of Pardubice

Abstract The study’s comparative aspect between the Faculty of Electrical Engineering and Informatics (FEI) and the Faculty of Chemical Technology (FChT) buildings is pivotal for elucidating nuanced differences in energy consumption practices within academic institutions, facilitating targeted energy efficiency measures. The research methodology adopts a comparative case study approach to examine two focal research buildings. Analysis entails computing energy consumption intensity per unit area for both buildings and comparing energy consumption data to identify disparities. The need for understanding energy consumption patterns and improving energy efficiency in academic infrastructure is directly addressed through variables studied in the comparative analysis, including energy consumption comparison, annual and monthly variations, ratio, and intensity. The data indicates that the average heating intensity in the FChT building surpasses that of the FEI building by a factor of 2.3, signifying significant disparities in heating energy consumption trends between the two structures and suggesting potential areas for targeted energy efficiency interventions. Moreover, the average electrical intensity in the FChT building is over 1.7 times higher than in the FEI building. Interestingly, despite the FChT building being 2.6 times larger than the FEI, its intensity does not always correspond with the increase in building area, indicating additional factors influencing energy consumption.

考虑作业工况特性的纯电动园艺作业机械动力传动系统参数匹配方法研究

To meet the economy requirement of the horticultural machinery, a two-speed transmission system was proposed based on the characteristics of the driving cycle of the horticultural machinery. Firstly, the test cycle of the horticultural machinery was established based on the data collector that fixed on the machinery. Secondly, the two-speed driveline system was designed. To reduce the energy consumption in the horticultural machinery working cycle, the gear ratio of the two-speed gear box was optimized with the goal of minimum energy consumption by the Genetic Algorithm. The optimized gear ratio were 11.6 and 9.62. The comparison of energy consumption between single gear ratio and two-speed gear ratio was made. The comparison result showed that the energy consumption can reduce 0.25% under one transportation test condition, the energy consumption can reduce 0.41% under one ploughing test condition, and the energy consumption can reduce 0.41% under one rotary test condition.

Artificial intelligence detection system based on multi-sensor and wireless communication

In today’s world, Internet technology and wireless communication technology are becoming more and more mature, which has brought massive network information resources to all sectors of society. At the same time, the phenomenon of data loss caused by illegal network intrusion is becoming more and more common. Therefore, it is necessary to identify and deal with them in combination with IDS (intrusion detection system). The problem of data processing is also very important. Enterprises can build a data management system according to their own needs, and use this system to process data. With the help of science and technology, AI (Artificial Intelligence) technology has become more mature and applied in many industries. Therefore, this paper proposed to build an AI IDS, and combined the deep RL (reinforcement learning) algorithm to analyze the performance of the system. This paper tested and analyzed the system from the aspects of precision and recall. The experimental results showed that the average precision of the five data sets was 94.76%, and the average recall rate was 91.4%. From the above data, combined with the algorithm in this paper, the precision and recall of the system have been significantly improved. This paper also conducted benchmark energy consumption comparison experiments for different cloud data management systems. The results showed that in terms of loading, the benchmark energy consumption of HBase was the lowest, which was 86KJ. In terms of query, the benchmark energy consumption of GridSQL was the lowest, which was 56KJ. It can be seen that different systems have their own advantages in the benchmark energy consumption of loading and query.

Mechanochemical Synthesis of Corannulene Flanked N-heterocyclic Carbene (NHC) Precursors and Preparation of Their Metal Complexes.

The synthesis of new compounds is an important pillar for the advancement of the field of chemistry and adjacent fields. In this regard, over the last decades huge efforts have been made to not only develop new molecular entities but also more efficient sustainable synthetic methodologies due to the increasing concerns over environmental sustainability. In this context, we have developed synthetic routes to novel corannulene flanked imidazolium bromide NHC precursors both in the solid-state and solution phases. Our work presents a comprehensive comparative study of mechanochemical routes and conventional solution-based methods. Green metrics and energy consumption comparison were performed for both routes revealing ball-milling generation of these compounds to be an environmentally greener technique to produce such precursors compared to conventional solvent-based methods. In addition, we have demonstrated proof-of-concept of the herein reported corannulene flanked NHCs to be robust ligands for transition metals and their ligand substitution reactions.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

A peer-and self-group competitive behavior-based socio-inspired approach for household electricity conservation

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 house monthly energy consumption datasets are collected for Maharashtra state, along with a questionnaire-based survey that includes socio-demographic information, household appliances, family size, and some other parameters. After data collection, the pre-processing technique is applied for data normalization, and correlation technique-based key features are extracted. These features are used to classify different house categories based on consumption. A knowledge-based system is designed based on historical datasets for future energy consumption prediction and comparison with actual usage. These comparative studies provide a path for knowledgebase system design to generate monthly energy utilization reports for significant behavior changes for energy savings. Further, Linear Programming and Genetic Algorithms are used to optimize energy consumption for different household categories based on socio-demographic constraints. This will also benefit the consumers with an electricity bill evaluation range (i.e., normal, high, or very high) and find the energy conservation potential (kWh) as well as a cost-saving solution to solve real-world complex electricity conservation problem.

Optimization Strategies for the Envelope of Student Dormitories in Hot Summer and Cold Winter Regions: Multi-Criteria Assessment Method

Energy consumption in student dormitories, key living and study spaces, is a major concern for institutions and communities. This paper proposes a multi-objective optimization model to address the issue of incomplete single-dimensional analysis in existing research. Firstly, optimization was conducted separately for the external walls, windows, and roof to study different parts of the building envelope. Secondly, a student dormitory in a hot summer and cold winter region was used for a comprehensive optimization analysis. The study compared energy consumption, carbon emissions, and costs with the original building, showing a 31.79% reduction in energy savings (ESR), while carbon emission savings (CESR) and cost savings (CSR) increased by 57.18% and 15.58%. This study highlights the importance of selecting appropriate window configurations for sustainability. Optimized thermally broken Low-E glass windows save 5.6% in annual energy consumption compared to aluminum alloy double-glazed windows, with only a 0.03% increase in energy consumption and a 4.49% rise in costs. Long-term, optimized windows provide greater positive feedback for energy efficiency. This case study offers insights for retrofitting buildings with good wall performance but poor window performance and emphasizes the comprehensive decision-making authority of designers and policymakers in sustainable renovations.

Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System

This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism emerges as the linchpin in surmounting this hurdle. Thus, this paper conducts a comprehensive comparison of energy consumption between two distinct consensus mechanisms: Proof of Work (PoW) and Quorum-based Byzantine fault tolerance (QBFT). Furthermore, an assessment of the most energy-efficient algorithm is performed across multiple networks and various parameters. This approach ensures the acquisition of reliable and statistically significant data, enabling meaningful conclusions to be drawn about the system’s performance in real-world scenarios. The experimental results show that, compared to the PoW, the QBFT consensus mechanism reduced the energy consumption by an average of 5%. This finding underscores the significant advantage of QBFT in addressing the energy consumption challenges posed by resource-constrained IoT devices. In addition to its inherent benefits of privacy and block time efficiency, the Quorum blockchain emerges as a more sustainable choice for IoT applications due to its lower power consumption.

Life cycle assessment as a driver for process optimisation of cellobiose lipids fermentation and purification

PurposeCellobiose lipids (CL) are biosurfactants produced by various Ustilaginaceae species in aerobic fermentations. They show high potential for application as alternatives to conventional oleochemical- or petrochemical surfactants. To ensure their environmentally friendly performance, we aimed to assess CL production from a life cycle perspective at an early developmental stage to identify process steps that have the highest impact on the environment. With this information, optimisation approaches can be derived.Materials and methodsFollowing a cradle-to-gate approach, we modelled the CL fermentation and purification process based on experimental data from the lab scale and process simulation data at a 10 m3 scale. For LCA, the impact categories (IC) abiotic depletion potential (ADP), eutrophication potential, photochemical ozone creation potential, global warming potential, acidification potential, and the primary energy demand were calculated for all process steps. Based on the obtained results, process bottlenecks were identified, and alternative process scenarios varying the related process parameters were simulated. These were used to assess the environmental impact reduction potential (EIRP) of an optimised process and draw recommendations for experimental process optimisation.Results and discussionThe obtained results showed that the fermentation caused ~ 73% of ADP and more than 85% of all other ICs. The major contributor was the electricity consumption for continuous fermenter aeration. Thus, reducing the fermentation duration from the initial 14 to 5 days would result in a decrease in all investigated ICs of up to ~ 27–52%. An increase in CL concentration results in a decrease in all ICs of a similar magnitude due to the higher yield per batch at comparable energy and material consumption. Although the share of purification process steps to all ICs is overall relatively small, implementing foam fractionation for in situ product recovery showed an additional EIRP of 18–27% in all purification IC shares.ConclusionsThe conducted LCA showed that overall, more EIRP can be achieved by optimising fermentation process parameters compared to purification process steps. This is mainly due to the long fermentation duration and large energy consumption for fermenter aeration. This highlights the importance of using LCA as a driver for process optimisation to identify process steps with high EIRP. While some of the results are specific to CL, other obtained results can be transferred to other fermentations.

Design and Experiments of the Data Acquisition System for Bale Rolling Characteristic Parameters on a Large-Scale Round Bale Machine

The parameters of the roll characteristics of a large-scale round bale machine were collected in real time to investigate the bale rolling mechanism. This investigation develops a set of adaptable and highly integrated data acquisition systems for the bale rolling performance parameters of large-type round bale machines. A rolling experiment is conducted using sunflower straw as the material, and the power consumption and radial tension of the roller-round bale machine during the bale rolling process are studied. In the grass core formation stage, the round bale machine’s torque need was minimal, the radial tension of the bale remained nearly constant, and the bale chamber was primarily filled with loose sunflower straw. The motor torque and the straw bale’s radial tension both showed a tendency of gradual increase when the round bale machine was in the grass-filling stage. The motor torque and bale radial tension displayed a roughly linear trend of rapid rise as the sunflower straw continued to enter the rolling bale chamber; this was when the round bale machine was in the compressed bale rolling stage. When the power consumption of the round bale machine was measured using the data acquisition system during the test bench empty run and core-creation stage, the energy consumption comparison analysis produced a relative error of 5.8%. During the stage of bale rolling and compression, the data acquisition system monitored the power consumption of the round bale machine. The relative error was 9.5%. The data acquisition system of the round bale machine test bed has an accuracy of 90.5%–94.2% when measuring the machine’s power consumption, indicating that it is a stable and efficient system. This study provides a foundation for further research on intelligent the roller-round bale machine.

CHAMBER FURNACE FOR SIMULATION OF CARBON MATERIALS HEAT TREATMENT IN ELECTROTHERMAL FLUIDIZED BED

Existing laboratory furnaces for high-temperature heat treatment of materials at around 3000°C, which have graphite heaters, allow samples to be heated at a rate two orders of magnitude lower than in electrothermal fluidized bed furnaces. This makes it impossible to use them in the research into the heat treatment of carbon materials because they do not meet the conditions of shock heating and do not allow to keep accurate residence time. The paper presents the research into the use of low-density composite carbon materials as heaters. Based on the results obtained, a high-temperature chamber furnace was designed, studied, and the temperature field in the heater was simulated. As a result, a high-temperature laboratory furnace was created with a sample heating rate of 20 °C/s - 100 °C/s, which made it possible to use it to simulate the heating process in the electrothermal fluidized bed furnace. Comparison of energy consumption in the furnace with a traditional graphite heater and a low-density carbon composite heater showed that heat loss in the furnace is reduced by 1.7 to 1.8 times.

Absorption Chiller Efficiency Increasing at Waste Heat Recovery of Cogen-eration Plants using a Vapor Compression Chiller as a Backup Cold Source

Cooling efficiency in a trigeneration cycles is one of the most frequently problems. A single-stage hot water absorption chiller is a universal solution for the gas engine waste heat utilization. How-ever, trigeneration centers exploitation experience shows the need for a more detailed technical and economic analysis to achieve maximum efficiency of the three-generation cycle. The purpose of this article is to substantiate the use of two-stage absorption cycles increasing the utilization rate of waste gas heat. The study presents the results of assessing the efficiency of using energy resources on the example of a three-generation center with more than 14 years of exploitation experience, including two single-stage absorption chillers (Q0= 0.65 MW each) and a chiller with a screw compressor (Q0= 0.8 MW). A comparison of actual energy consumption with designed indicators was carried out. The influ-ence of external and internal factors on the efficiency of heat recovery from a gas engine in single-stage absorption refrigeration machines with a water-heated generator is considered. The dependence of the change in the average annual cooling coefficient during the calendar year was obtained. Based on the study, proposals were formulated to improve the energy efficiency of refrigeration supply, giving an increase in refrigeration capacity of 6.1% with a commensurate reduction in the load on the screw chiller. The option of using a combined two-stage cycle chiller (type 2) under similar operating conditions showed a COP=1.43, which provides savings of 67.45 MWh of electricity or 5.4%, on an annualized basis (assuming only waste heat is used). Additional advantages of such a cycle include increased accu-racy and stability of maintaining the water temperature at the absorption chiller evaporator due to more flexible adjustment of the temperature of the lithium bromide solution in the high-temperature generator.

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.