Energy Consumption In Cities Research Articles

Urban Metabolism of Energy Consumption in Cities

جاءت فكرة البحث من اهمية موضوع استهلاك الطاقة في المدن حيث تواجه مدن العالم بشكل عام والمدن العراقية بشكل خاص تحديات بيئية كبيرة من تصاعد نسبة الانبعاثات الكربونية مما يستوجب ايجاد الحلول اللازمة من خلال فهم عملية مدخلات ومخرجات المدينة المتمثلة بعملية الايض الحضري والغازات الدفيئة وانواع مصادر الطاقة وقياس بصمة الطاقة لمنطقة الدراسة وعلاقة استعمالات الارض في استهلاك الطاقة وتوصل البحث الى عرض النتائج التي تعطي مؤشر الى زيادة مستمرة في استهلاك الطاقة في المدن العراقية وتباين بين استعمالات الارض المختلفة من حيث استهلاك الطاقة والموارد ويفرض البحث ان اعتماد استراتيجيات تخطيطة مستدامة يمكن ان تقلل من ذلك الاستهلاك وبالتالي تقلل من بصمة الطاقة والانبعاثات الكربونية.

The effect of industrial upgrading on energy consumption

Energy conservation is a key way for China to achieve the goal of “carbon peaking” and “carbon neutrality” goals. Based on the panel data of 152 resource-based and old industrial cities (RBOI cities) in China from 2010 to 2021, this study explores the effect of national industrial transformation and upgrading demonstration zones (ITUDZ) on city's energy consumption and its mechanism channel using a time-varying difference-in-differences method. The results show that: (1) The ITUDZ significantly reduce the city's energy consumption. And the result still robust after a series of robustness tests. (2) Mechanism analyses show that the ITUDZ mainly rely on optimizing the industrial structure and improving the green innovation to reduce energy consumption. Moreover, this energy-saving effect can be enhanced by the market-oriented reform. (3) Heterogeneity analyses find that the energy-saving effect of ITUDZ on the eastern China and multi-city zones are significantly stronger than that of the central-western China and single-city zones. This study provides new evidence to understand the effect of industrial place-based policy on energy conservation. It provides a reference for the realization of green development in China.

The spatial dimension of energy consumption in cities

Most global energy consumption is spatially bounded in cities, leading to an urgent need to reduce urban energy demand and greenhouse gas emissions. This study delves into the spatial dimension of energy consumption, focusing on urban areas' physical and functional parameters. The goal is to define a model to support decision-making for formulating public policies that contribute to promoting strategies for energy efficiency. The theoretical rationale is to consider energy consumption as a space-based process that needs to be legible for policymakers and planners. The spatialization of energy consumption relating to urban morphologies, densities and uses is pivotal to understanding the territory as a multidimensional system in transition. The methodology addresses a Geographic Information System-based approach that delimits morphological patterns to classify urban areas according to energy consumption zoning. The “morpho-energetic analysis” presented is an approach that could provide policy guidance to decision-makers and planners. Using a municipality in Portugal as case study, results indicate that urban morphology permits identifying where and how energy efficiency can be improved, providing a strategic framework to support the definition of energy policies, which leads to the construction of regulations and urban planning measures for dealing with the sustainable energy transition in cities.

Using the Degree-Day Method to Analyze Central Heating Energy Consumption in Cities of Northern China

In the context of global population growth and energy scarcity, building energy consumption has become a critical issue with implications for the sustainable development of human society. Winter heating consumption constitutes a large portion of total energy used in buildings, especially in regions with cold climates. This paper employs the degree-day method to analyze the energy consumption of central heating in northern Chinese cities. The study sample consists of 60 target cities, including 30 located in severe cold regions and the remaining 30 in cold regions. By utilizing heating energy consumption and climate data from 2019, the relationships between heating intensity (kWh/m2) and heating degree days (HDDs) are established for the selected cities. Additionally, statistical analysis and model comparisons are conducted. The results show strong positive correlations between heating intensity and HDDs in both severe cold regions and cold regions, with the actual heating base temperatures for the two regions being 21 °C and 22.3 °C, respectively. Moreover, the deviation index of heating intensity is introduced to analyze the energy consumption characteristics of central heating in northern cities from three perspectives: city size, level of heating development, and geographical regions. The analysis suggests that cities with large population, strong economies, and high levels of development exhibit better energy-saving performance. Lastly, several improvement suggestions are proposed to address the potential problems related to energy conservation of central heating systems in cities of northern China.

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

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.

A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines

Policies for reducing CO2 and other GHG emissions have motivated an increase in electrification in metropolitan areas, mandating reductions in energy consumption. Metro systems are keystone contributors to the sustainability of cities; they can reduce the energy consumption of cities through the use of the economic driving parameters in their onboard automatic train operation systems (ATO) and through the strategic design of efficient timetables. This paper proposes a two-level optimization method to design efficient, comfortable, and robust driving commands to be programmed in all the interstations of a metro line. This method aims to increase the sustainability of metro operations by producing efficient timetables with economic driving for each interstation while considering comfort restrictions and train mass uncertainty. First, in the eco-driving level, an optimal Pareto front between every pair of successive stations is obtained using a multi-objective particle swarm optimization algorithm with fuzzy parameters (F-MOPSO). This front contains optimized speed profiles for different running times considering train mass variations. The global problem is stated as a multi-objective combinatorial problem, and a fuzzy greedy randomized adaptive search procedure (F-GRASP) is used to perform an intelligent search for the optimal timetables. Thus, a global front of interstation driving commands is computed for the whole line, showing the minimum energy consumption for different travel times. This method is analyzed in a case study with real data from a Spanish metro line. The results are compared with the minimum running time timetable and a typical timetable design procedure. The proposed algorithms achieve a 24% reduction in energy consumption in comparison to the fastest driving commands timetable, representing a 4% increase in energy savings over the uniform timetable design.

Research on the coordinated control of large permanent magnet direct drive fan and air conditioner based on finite element method

In recent years China has committed to energy saving and emission reduction. Chinese Government has proposed a strategic policy of carbon neutrality. In the context of energy shortage, the development of sustainable cities and green buildings is a major trend. An important part of energy consumption in cities is the energy consumed by temperature regulation in buildings, and reducing this part of energy consumption can be of great help to the development of sustainable cities. In this paper, our team propose a scheme for temperature regulation with large permanent magnet direct drive fans system and air conditioners. Compare the conventional room temperature regulation scheme which only uses air conditioners with the new room temperature regulation scheme which uses the large permanent magnet direct drive fan and air conditioners by simulating the indoor temperature regulation process. The results of simulation show that the new scheme’s temperature regulation effects are more uniform than the conventional scheme. The new scheme can reach the target Comfort Index of Human Body faster than the conventional scheme, and the energy consumption can be reduced by more than 8%. The paper concludes that the new room temperature control solution is more energy efficient, reduces carbon emissions and helps to achieve the carbon neutrality target.

A novel discrete deep learning-based intelligent methodology for energy consumption classification

Energy consumption classification is one of the most widely-used approaches in the energy area that is applied in various applications such as household, commercial, urban, rural, industrial, etc. Energy consumption due to its substantial positive influence on the quality of made decisions, production and distribution management, and cost reduction, has considerable literature. In the classification literature, numerous statistical, intelligence, and hybrid methods have been developed and proposed in order to yield more accurate results. The common point of all these models is that whole of techniques follow a prevalent and repetitive procedure of learning process based on continuous distance-based cost function. While the objective function of classification is discrete. While the mismatch of the continuous cost function in classification problems with the discrete objective function causes them to be illogical or inefficient. Therefore, in this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. In this paper, deep multilayer neural networks (DMNs) have been exemplarily applied to implement the proposed learning algorithm. Although more consistency of goal and cost functions will logically has not a negative effect on the classification rate of classifiers. However, in this paper, to show the superiority of the proposed discrete deep multilayer neural network (DDMN) over conventional continuous-based classifiers, four energy consumption benchmark data sets have been employed. These data sets include the Tamilnadu Electricity Board Hourly Readings, Tetouan City Energy Consumption, Household Electric Power consumption, and steel industry Energy Consumption. Empirical results indicate that the proposed DDMN model, as pre-expected, can yield better performance in all cases than the DMN model. The DDMN model can achieve a 93.27% classification rate on average and upgrade its classic version approximately by 4.77%.

Graph-Based Computational Methods for Efficient Management and Energy Conservation in Smart Cities

Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street lighting is one of the most significant contributors to urban power consumption. This paper presents a summary of recent attempts to use computational methods to reduce energy usage by lighting systems, with special focus on graph-based methods. Such algorithms require all the necessary data to be integrated, in order to function properly: this task is not trivial, and is very time-consuming; therefore, the second part of the paper proposes a novel approach to integrating urban datasets and automating the optimisation process. In two practical examples, we show how spatially triggered graph transformations (STGT) can be used to build a model based on the road network map, sensor locations and street lighting data, and to introduce semantic relations between the objects, including utilisation of existing infrastructure, and planning of development to maximise efficiency.

Smart Street Lighting System

Street lighting is an important part of the energy consumption of cities. With suitable smart lighting systems that increase energy efficiency, it is possible to save on lighting costs substantially. Intelligent street lighting systems generally consist of lamps, ballast and dimming devices located in the area to be illuminated, control units placed in different lighting zones, and central management systems that can manage and monitor these regional controls. With smart lighting systems suitable for energy efficiency, it is possible to save a great deal of lighting costs while protecting the environment. When current projects are examined, it is seen that up to 80% energy savings can be achieved when automation systems are used in street lighting. There is a need for an approach that controls the luminous flux without reducing the driver's safety and comfort, and that performs dimming studies using machine learning methods. In this paper, the smart street lighting system developed jointly by ISBAK and Panasonic is explained.

A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.

Principal component analysis of the urban building morphology of the Metropolitan Area of Mendoza, Argentina

Cities located in arid areas are facing several risks that threaten their sustainability due to the effects of climate change and urbanization, and the resulting consumption inequality and depletion of natural resources. There are many variables that determine the urban-building form, which, in turn, affects energy consumption in cities. Therefore, the goal of this work is to condense the information provided by the urban-building morphological variables into just a few variables or combinations for the urban blocks located in the six departments of Mendoza’s Metropolitan Area (MMA). Methodologically, quantitative data of the spatial distribution of urban-building variables were considered, their correlations were calculated, and a Principal Component Analysis was applied to synthesize the number of variables. The results identify the Building Density, Building Separation, and Normalized Difference Vegetation Index (NDVI) as the main variables of the first principal component, in five of the six departments of the MMA. The first two are related to the built environment, and the third is to vegetation. By including data on urban tree cover, available only for the Capital department, the results include the tree-cover magnitude, completeness, and transmissivity variables, together with those already identified in the first three components. The findings of the work provide information on the representative urban-building variables of the oasis city that will allow, in the future, establishing intervention priorities considering a reduced number of synthetic variables, to propose efficiency and energy generation strategies.

Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain

The development of a green finance system can be important for the control of energy consumption. This research focuses on the effects of the spatial externality of institutional support from the green finance systems on the energy efficiency of Chinese cities. The Malmquist data envelopment analysis (DEA) model is used to analyze the efficiency of energy consumption. The spatial Durbin model is used to analyze the effects of the spatial externality of green finance on energy efficiency. The results show that green finance has an inhibitory impact on energy consumption. The interaction of the policy of green finance and the development of the digital economy also has a negative spatial externality effect on energy consumption in cities. In addition, the control of energy consumption can be further enhanced due to negative spatial autoregressive relationships with cities. The control of energy consumption in cities with large population can be further strengthened through the positive interaction between gross domestic product (GDP) and green finance. Cluster analysis is performed to analyze the characteristics of cities, and the stationary probability in the switch of clusters is forecasted by the Markov chain. Hidden Markov Chain Modeling (HMM) is applied to analyze the probability of city clusters based on hidden connection between institutional support for digital economy and development of green finance with energy consumption. This study can provide policy makers with a systematic comprehension of the impact of spatial externalities on energy efficiency by green finance system in the cross-functional digital economy.

Impact of Climate Change and Technological Innovation on the Energy Performance and Built form of Future Cities

The building and transportation sectors are responsible for the greatest proportion of energy consumption in cities. While they are intrinsically interlinked with urban built form and density, climate change and technological innovation are having an effect on their relative contributions. This paper aims to develop an optimisation framework to facilitate the identification of the most energy-efficient urban built forms and urban geometry for the future built environment that can be adapted to the changing climate and ongoing technological development. It examines future scenarios for the city of London as a temperate climate zone (as a case study), in 2050, and contrasts it with the present situation. Specifically, the impact of climate change along with the penetration of electric vehicles into the transportation system that can be charged via rooftop photovoltaics is investigated. This study initially develops the geometrical models of four selected urban built forms and, secondly, analyzes their energy performance using an urban energy simulation software. The results, showing the impact of future scenarios on building energy performance, urban built form and density, demonstrate that court and tunnel-court built forms show better energy performance for future development. It is therefore recommended that for future urban developments in London, deep plan court and tunnel-court buildings with a lower number of storeys and a large cut-off angle are more advantageous in terms of building energy to accommodate the expected climate change. Finally, results of simulation trials indicate that the total building energy demand in 2050 is considerably higher than in the present climate as a result of additional cooling load and electric vehicle charging load.

Spatial–temporal dynamics of the built environment toward sustainability: A material stock and flow analysis in Chinese new and old urban areas

AbstractThe urban built environment has accumulated massive amounts of resources, which is the main “hot spot” of construction materials and energy consumption in cities. To effectively monitor and manage resources use and waste disposal toward sustainability, this study intended to explore different metabolic paths of the urban built environment, as well as strategies for new and old urban areas separately with the help of material stock and flow analysis. Based on snapshots of 2003, 2010, and 2017 and combined with big data, a four‐dimensional geographic information system buildings database of Lixia District in Jinan, China, was compiled for bottom‐up stock modeling. Finally, the environmental impact assessment of the buildings’ demolition and disposal stage was carried out. The results showed that the building material stock was 142.31 Mt in 2017, with an increase of 69% from 2003. The reinforced concrete buildings increased rapidly from 2003 to 2017, especially in new urban areas, which has boosted demand for steel and cement by more than 150% during these 15 years. The material stock of the old urban area tends to be saturated, whereas the new urban area has experienced a rapid expansion with the material inflow four times as large as the material outflow. Potential recycling of waste concrete can reduce the generation of building demolition waste by 50%, save 0.15 km2 of land occupation, increase raw material substitution by nearly 10 times, and reduce CO2 emissions by 2.5 times.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, Surface Urban Heat Island (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, UV Aerosol Index (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m2) and CO (1.64 mol/m2) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m2). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

Estimation of city energy consumption in China based on downscaling energy balance tables

The accurate estimation of city-level energy consumption is of great significance for the formulation of energy conservation and emission reduction policies and for addressing global climate change. Previous studies can only provide the estimation of the total energy consumption rather than energy consumption by sector or energy type, and the estimation accuracy of these methods are rarely validated. Meanwhile, few studies on carbon emission accounting based on the energy balance table provide some enlightenment on energy consumption estimation by sector. However, these studies suffer from the problems of incomplete distribution indicator systems and discrepancies between the provincial total and city-level aggregation data, resulting in inaccuracy. To address these challenges, in this study, a systematic accounting method based on downscaling energy balance tables is developed to estimate the energy consumption of prefecture-level cities in China. With this method, we compiled the energy consumption inventory of 286 cities in 2003–2019, covering 18 energy activities and 30 fossil fuels. To completely evaluate the performance of the proposed method, we also provided a comparison of the estimation results with the inversion simulation method by nighttime light data and the previous top-bottom allocation method. The comparison results reveal that the proposed method outperforms all the other approaches with a small average error, better stability and higher accuracy. Overall, this method provides an effective way to obtain continuous, long-term and full-coverage energy consumption data in cities, which provides reliable data support for the formulation of rational energy conservation and emission reduction policies at the city level.

Sensing global changes in local patterns of energy consumption in cities during the early stages of the COVID-19 pandemic

COVID-19, and the wider social and economic impacts that a global pandemic entails, led to unprecedented reductions in energy consumption globally. Whilst estimates of changes in energy consumption have emerged at the national scale, detailed sub-regional estimates to allow for global comparisons are less developed. Using night-time light satellite imagery from December 2019–June 2020 across 50 of the world's largest urban conurbations, we provide high resolution estimates (450 m2) of spatio-temporal changes in urban energy consumption in response to COVID-19. Contextualising this imagery with modelling based on indicators of mobility, stringency of government response, and COVID-19 rates, we provide novel insights into the potential drivers of changes in urban energy consumption during a global pandemic. Our results highlight the diversity of changes in energy consumption between and within cities in response to COVID-19, moderating dominant narratives of a shift in energy demand away from dense urban areas. Further modelling highlights how the stringency of the government's response to COVID-19 is likely a defining factor in shaping resultant reductions in urban energy consumption.

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

With the economic development in recent years, smart cities have also risen. Smart city is based on the next generation of innovation in the knowledge society, and the use of new generation information technology in all areas of the city is conducive to the improvement of the quality of urbanization and the quality of citizens’ lives. This article aims to study the significance of the reduction algorithm in the energy consumption analysis of smart cities. This article puts forward the problem of how to solve the problem of huge energy consumption in smart cities. In smart cities, the related objects that generate energy consumption mainly include industry, agriculture, and transportation construction. People’s lives are ubiquitous in the generation of energy consumption. This paper analyzes the energy consumption of smart cities through the reduction algorithm and puts forward a plan on how to reduce the energy consumption of smart cities, which is meaningful for the rational use of resources and reducing the cost of smart cities.