Energy Consumption In Urban Areas Research Articles

Future pathways for green hydrogen: Analyzing the nexus of renewable energy consumption and hydrogen development in Chinese cities

Hydrogen energy offers a promising pathway for achieving zero-carbon urban energy consumption. This study quantitatively analyzes the patterns of hydrogen energy development and its linked to electricity consumption in China's urban areas. It investigates the spatial relationships between hydrogen energy expansion, the potential for green hydrogen production, and overall energy use. Utilizing a coupling coordination degree (CCD) model and a panel vector autoregressive (PVAR) model, the research explores the dynamic interactions between urban hydrogen energy development and the transition to renewable energy sources. In line with Chinese government policy, the study also conducts a scenario analysis of green hydrogen production by 2030, proposing a consumption strategy that balances environmental sustainability with economic growth. The key conclusions are as follows: (1) There is a spatial mismatch between the locations where hydrogen energy is being developed and where the potential for green hydrogen production is greatest. Cities with higher electricity consumption demonstrate stronger synergies in the coordinated development of hydrogen and renewable energy. (2) The development of hydrogen energy significantly accelerates the transition to renewable energy consumption in urban areas, with this impact being more substantial than that of the utilization capacity alone. (3) Hydrogen production in the Plan Scenario is significantly higher than in the Pessimistic Scenario, underscoring the critical importance of implementing renewable hydrogen projects in provinces with high production potential. Future trends in green hydrogen consumption at the provincial level indicate that addressing the challenges of efficient inter-provincial transportation of hydrogen energy remains essential.

Traffic light optimization with low penetration rate vehicle trajectory data

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world.

End-use energy projections: Future regional disparity and energy poverty at the household level in rural and urban areas of India

Regional disparity and energy poverty are major challenges for India on a macro-scale. In this study we explored regional disparity and energy poverty in India's future by formulating a series of unique mathematical functions while analyzing historical time-series of per-capita energy consumption (PEC). The functions formulated have important implications on India's approaches towards rapid urbanization, access to clean energy, and rural electrification. We also analyzed end-use energy consumption in low, mid and high-developing states and examined the impacts of the uncertainty of socio-economic changes. As per our estimates, energy consumption in urban areas, is likely to grow by 3.6 times from 2010 to 2070, whereas that in rural areas is likely to decrease by 0.6 times. Energy poverty among Indian states narrows by 2050, while energy consumption in the mid and high-developing states rises rapidly as urbanization and per-capita income increase. Even with this consumption growth, India's average household PEC in 2070 may remain lower than the world average recorded in 2010. The provision of advanced energy resources and technologies is therefore likely to be an important policy challenge for India, especially in the rural areas of low-developing states, as the country pursues its target of reducing energy poverty and regional disparity.

Main Pathways of Carbon Reduction in Cities under the Target of Carbon Peaking: A Case Study of Nanjing, China

As a designated national low-carbon pilot city, Nanjing faces the challenge of reducing energy consumption and carbon emissions while experiencing rapid economic growth. This study developed a localized Long-range Energy Alternatives Planning System (LEAP) model specifically for Nanjing and constructed four different development scenarios. By utilizing the Log Mean Divisia Index (LMDI) decomposition, the Tapio decoupling elasticity coefficient, and comparing the emission reduction effects of individual measures and their cross-elasticity of carbon reduction, this study investigated the key factors and their carbon reduction path characteristics in Nanjing toward its carbon peak target by 2030. The results indicate that: (i) Nanjing could reach its peak carbon target of about 3.48 million tons by 2025 if carbon reduction measures are strengthened; (ii) The main elements influencing Nanjing’s carbon peak include controlling industrial energy consumption, restructuring the industry, promoting the construction of a new power system, and developing green transportation; (iii) Controlling industrial energy consumption and changing industrial structure have a greater impact on reducing carbon emissions than other measures, and both have a synergistic effect. Therefore, Nanjing should prioritize these two strategies as the most effective methods to reduce carbon emissions. Additionally, to slow down the growth of urban carbon emissions, policies aimed at reducing the energy intensity and carbon intensity of energy consumption should be formulated. For instance, the integration and innovation of green industries within the city region, such as new energy vehicles, new energy materials, and big data, should be accelerated, and the proportion of clean energy consumption in urban areas should be increased. The LEAP (Nanjing) model has successfully explored Nanjing’s low-carbon pathway and provided policy guidance for the optimal transformation of industrial cities and early carbon peaking.

Urban anthropogenic heat index derived from satellite data

The increasing anthropogenic heat, which is emitted from human activities and energy consumption in urban areas and migrates to the atmosphere, significantly alters surface energy balances and the local climate and in turn generates urban heat islands (UHIs). Anthropogenic heat flux (AHF) has been estimated with energy balance residual and inventory methods. However, these methods have large uncertainty and fail to further explore the spatial distribution of anthropogenic heat at large scales due to limited data availability and the complexities of human activities, urban geometry, landscapes and building thermal properties. From the heat sources of the UHIs and based on thermodynamics, this study deduces that the AHE difference between urban and surrounding rural areas can be quantified as the residual of the heat for increasing UHIs and the heat difference from natural radiation between urban and rural areas. Thus, an urban anthropogenic heat index (UAHI) is proposed to quantify the contribution of AHF to UHIs, as a proxy of AHF. The UAHI is generated using the normalized differences in urban–rural land surface temperature (LST) and albedo derived from both Landsat and MODIS data. We investigate the spatial and temporal association between the UAHI and AHF in Beijing. The results demonstrated that the average annual UAHIs in Beijing ranged from 0.168 to 0.208 and steadily improved in the last decade. The UAHI can represent detailed spatial heterogeneity and temporal variations in AHF with correlation coefficients in the range from 35% to 50%. UAHI can also identify the dynamics of AHF hotspots and the heating and cooling sources in Beijing. Overall, the UAHI is a convincing indicator of the contribution of urban anthropogenic heat to UHIs, providing a new option for quantifying AHF dynamics and improving the knowledge of urban thermal environments.

Application of artificial intelligence and neural network technologies in the construction industry

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.

Awareness of Household Energy Consumption and its Impact on the Environment: A case of Kathmandu Valley, Nepal

The purpose of this paper is to assess the situation as it currently exists and the environmental impacts of household energy consumption in Nepal's urban setting. The paper makes several recommendations for joint environmental regulation and sustainable energy consumption. The study evaluated the level of sustainable energy consumption and examined the extent and composition of household energy use in urban areas based on questionnaire survey data. The findings indicate that electricity is the primary means of lighting, and LPG is the primary means of cooking. The findings also demonstrated that only a minority of those living in the Kathmandu Valley exhibit energy conservation behaviors and are cognizant of environmental effects like climate change. The overall energy consumption in urban areas is positively influenced by both population density and income levels. It is crucial to implement appropriate measures, such as optimizing the renewable resource and enhancing energy usage efficiency through technological advancement, in order to meet the goal of emission reduction and sustainable energy consumption.

Modernity in Javanese tradition: adapting vernacular design and local culture to Indonesian urban living

There is a growing demand for electricity in homes in Indonesia. In this study, traditional Javanese houses were analyzed and redesigned to suit Indonesian modern lifestyles. The new modern Javanese houses are used as benchmarks for evaluating energy-efficient designs in Jakarta, Indonesia. It was compared to several rumah type 36’s models in order to analyze it. The results show that a modern Javanese house can reduce its total electricity consumption by 31%. There is a high consumption of electricity in home appliances related to cooking and miscellaneous appliances. Heat can be faster transferred from these cooking appliances by designing an open layout and atrium. These design can lowered the nighttime heat temperature by about 5°C compared to the daytime temperature, and by about 1.6 °C compared to the temperature outdoors. Energy consumption in urban areas can be reduced by adapting a traditional house concept to modern homes.

Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas

As a new mode of transportation in the future, electric vertical take-off and landing unmanned aerial vehicles (eVTOL UAV) can undertake the task of logistics distribution and carry people in urban areas. It is challenging to carry out research designed to plan the path of eVTOL UAVs which can have a safe and sustainable operation mode in urban areas. Therefore, this work proposes a method for planning an obstacle-free path for eVTOL UAVs in urban areas with the goal of minimizing energy consumption. It aims to improve the safety and sustainability of eVTOL UAV operations. Based on variations of air density with height, a more accurate formula for calculating battery energy consumption of eVTOL UAV is derived. It is used in the vertical takeoff and landing phase and horizontal flight phase, respectively. Considering the influence of buildings on eVTOL UAV operation, a path planning method applicable to complex urban environments is proposed. The safe nodes of eVTOL UAV flight are obtained by using Voronoi diagrams based on building locations. Then, the complete shortest and obstacle-free path is obtained by using a Dubins geometric path and Floyd algorithm. After obtaining the obstacle-free paths for all flight height zones, the battery energy consumption of the eVTOL UAV in each flight height zone is calculated. Then, the flight height with the minimum energy consumption is obtained. The simulation results show that the path length obtained by the proposed path planning method is shorter than that obtained by particle swarm optimization; the total battery energy consumption changes in the same pattern in the low-altitude areas and high-altitude areas; the difference between the maximum and minimum energy consumption in the small area enables the eVTOL UAV to cover about 350 m more, and about 420 m more in the large area. Therefore, in future high-frequency UAV mission flights, choosing the altitude with the lowest energy consumption can reduce UAV operator costs. It can also significantly increase UAV transport range and make UAVs operate more sustainably.

Household Energy Consumption Patterns and Carbon Emissions for the Megacities—Evidence from Guangzhou, China

Over the last 20 years, energy consumption in the residential sector in China has grown rapidly, and the growth is faster than that of any other energy form. To assess the limitations of the spatial characteristics of household energy consumption in urban areas, this paper selected Guangzhou as the research area. Specifically, the old town, core area, central area and peri-urban areas, which best reflect the evolutionary characteristics and spatial differentiation of households, were assessed. Based on the surveyed database of community-scale household energy consumption (N = 1097), the spatial heterogeneity of household energy consumption and carbon emissions at the community scale were assessed through exploratory spatial data analysis and the standard deviation ellipse method. The results report that (1) the main sources of energy consumption in Guangzhou households were water heating equipment, kitchen equipment and refrigeration equipment, which were related to the climatic conditions and cultural traditions of the city. (2) There was significant spatial heterogeneity in the carbon emissions from household domestic energy use in Guangzhou. (3) The economic level, the effects of the Lingnan culture and the characteristics of residents are the main drivers influencing the spatial characteristics of household energy consumption and carbon emissions in Guangzhou. We propose that policy development should actively promote energy-efficient equipment, such as water heating and cooling equipment, in urban households and take full account of the basic household energy needs of residents in old urban and suburban areas while promoting the development of low-carbon buildings.

Energy effect of urban diversity: An empirical study from a land-use perspective

Rapid urbanization not only consumes substantial amounts of valuable land resources but also changes the pattern of land-use. Diversity of land use measures the functional composition of urban land and reflects the structure of urban activities from a land perspective. This paper explores the relationship between urbanization and energy use, by empirically investigating the impact of land use diversity on energy consumption in urban areas with a panel dataset of prefecture-level cities in China. The instrumental variables method is employed to address the endogeneity of land-use diversity. It is found that the land-use diversity of China's cities has exerted an energy-saving effect, while the effect only works for medium-sized cities. The findings imply the possibility of promoting urban energy saving by moderate control of city size and diversity of land use.

Energy efficiency through façade design model of Nobel house building

One of the triggers to global warming is the increase of CO2 trace caused by fossil energy consumption. The escalation of fossil energy consumption in urban area comes from industrial sector, housing sector, and commercial sector. Commercial buildings in urban area play a major contribution in CO2 production level. That is why, electricity efficiency efforts are needed. The high energy consumption in commercial buildings are commonly caused by less precise façade construction, either on its design, material, and orientation. The objective of this research is to evaluate the effect of façade design on energy efficiency level at Nobel House Building. The participation of Nobel House to lower CO2 is actually an effort to improve GBCI green building certification valuation result, from Gold to Platinum. Energy aspect is a priority in certification valuation to improve their rank. The research method utilized in this research is a descriptive qualitative method with OTTV analysis on 15 Nobel House Building façade models. The research is further narrating the initial 15 models with gold certifications. The result of this research is used to determine 4 façade model with energy efficiency below the base line. (SNI 6389-2011, GBCI,2013) with two main considerations which are easy in construction and maintenance work.

Urban Energy Data Visualization and Management: Evaluating Community-Scale Eco-Feedback Approaches

AbstractAs energy consumption in urban areas rises to the level of being a global concern, the impact of community behavior becomes critical to assessing and managing energy consumption. Eco-feedba...

THE ESTIMATION AND RELATIONSHIP OF DOMESTIC ELECTRICITY CONSUMPTION AND APPLIANCES OWNERSHIP IN MALAYSIA INTERMEDIATE CITY

The growing trend of energy consumption in urban area has created negative impacts to the urban environment because the issues related to energy use and urban developments are largely dependent on human aspects and mobilized by the inhabitants of the city itself. This study aims to estimating the electricity consumption with regards to electrical appliances usage and carbon emissions in domestic sector of urban area. The study revealed that average electricity power consumption was 648.31 kWh. Meanwhile, refrigerator and air conditioning recorded as the highest electricity consumption appliances compared to other appliances. In terms of type of house, household from bungalow house consumed more electricity power and resulted to high carbon emissions compared to other households. The study suggested that the level of knowledge, awareness, commitment, attitude and behavior of electricity consumption among household should be intensified. Besides, the number of energy saving appliances also needs to be improved and encourage smart energy consumption that led to the sustainable urban development. Keywords: Electricity Consumption; Appliances; Carbon EmissionsJEL Classifications: Q40; R20DOI: https://doi.org/10.32479/ijeep.8358

Dynamic Convergence of Green Total Factor Productivity in Chinese Cities

China’s energy consumption in urban areas accounts for a large proportion of total energy consumption, and many pollutants are emitted with the energy consumption. Considering the requirement for green development of economy, it is necessary to study the green total factor productivity (GTFP) in cities. In this study, the Malmquist index, spatial autocorrelation analysis and convergence analysis are used to analyze the GTFP for 263 prefectural or higher-level cities in China. The results show a growing trend of values measured by the GTFP in Chinese cities, indicating an increase in efficiency. In addition. the eastern region has the highest efficiency, followed by the central region while the lowest being the western region. The calculated values of GTFP show a relatively strong overall spatial clustering with some local high-high clusters of high index values. GTFP also shows relatively weak divergence and no sign of convergence. Thus, we propose that, to improve GTFP and narrow the gap between regions, it would be necessary to enhance technological progress and restructuring industrial productivity in cities.

Warmer Environments Increase Implicit Mental Workload Even If Learning Efficiency Is Enhanced.

Climate change is one of the most important issues for humanity. To defuse this problem, it is considered necessary to improve energy efficiency, make energy sources cleaner, and reduce energy consumption in urban areas. The Japanese government has recommended an air conditioner setting of 28°C in summer and 20°C in winter since 2005. The aim of this setting is to save energy by keeping room temperatures constant. However, it is unclear whether this is an appropriate temperature for workers and students. This study examined whether thermal environments influence task performance over time. To examine whether the relationship between task performance and thermal environments influences the psychological states of participants, we recorded their subjective rating of mental workload along with their working memory score, electroencephalogram (EEG), heart rate variability, skin conductance level (SCL), and tympanum temperature during the task and compared the results among different conditions. In this experiment, participants were asked to read some texts and answer questions related to those texts. Room temperature (18, 22, 25, or 29°C) and humidity (50%) were manipulated during the task and participants performed the task at these temperatures. The results of this study showed that the temporal cost of task and theta power of EEG, which is an index for concentration, decreased over time. However, subjective mental workload increased with time. Moreover, the low frequency to high frequency ratio and SCL increased with time and heat (25 and 29°C). These results suggest that mental workload, especially implicit mental workload, increases in warmer environments, even if learning efficiency is facilitated. This study indicates integrated evidence for relationships among task performance, psychological state, and thermal environment by analyzing behavioral, subjective, and physiological indexes multidirectionally.

Quantifying seasonal and diurnal contributions of urban landscapes to heat energy dynamics

Cooling energy consumption in urban areas is affected significantly by the dynamics of urban heat flux. However, we still lack a clear understanding of the quantitative contribution rate and underlying mechanism of typical urban landscapes to urban heat dynamics, especially in seasonal and diurnal patterns. Here we used a thermal infrared camera and portable meteorological instruments to examine the sensible heat flux (SHF) changes of five typical urban landscapes in Beijing based on surface temperature and concurrent microclimate conditions. Diurnal and seasonal variations of SHF were quantified by comparing changes in forenoon and afternoon in different seasons. Results showed that (1) walls and roads act as heat-source, while forests and water act as heat-sink in all seasons; however, grassland served as heat-sink in summer and spring-autumn, but it becomes a heat-source in winter. (2) The seasonal variation of sensible heat flux of the wall is the greatest, followed by water, while that of trees is the smallest. Besides, the highest sensible heat flux and the maximum variation among typical urban landscapes occur between noon and 2:00 pm. (3) The numerical contribution rate of typical landscapes to sensible heat flux varies with daytime (forenoon and afternoon) and seasonal changes, and these ratios can be used as parameters to adjust the numerical models to obtain more reliable results in surface-energy-flux-related studies. The results of this study can provide a reference for explaining controversial findings based on remote-sensing data, and provide insights into revealing the sensible heat flux mechanism of typical urban landscapes and cooling energy conservation in cities.

Empirical study on the impact of China's metro services on urban transportation energy consumption

The development of metro systems has shortened the travel distances and density within cities, and promoted the transformation of urban transportation, which has had an impact on traffic energy consumption. Based on data from 81 cities in mainland China with a permanent population of more than one million from 2003 to 2016, this paper uses the difference in differences (DID) method to study the impact of urban metro systems on transportation energy consumption in China. The results show that after the opening of the city subway, per capita traffic energy consumption decreased, indicating that subways reduce energy consumption in urban areas; there exists a “U” relationship between metro operation intensity and per capita traffic energy consumption. The average level is far from the inflection point value, which indicates that an increase in metro operation intensity has the effect of improving traffic energy consumption. However, the marginal benefit of reducing urban traffic energy consumption is gradually decreasing. Further research has found that the metro system can reduce the distance travelled by cars by replacing the use of traditional automobile transportation, thus reducing transportation energy consumption.

City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique

Growing energy consumption in urban areas has increased the importance of planning for future energy systems. Thus, improving the modeling abilities for predicting energy consumption at the city scale is critical. In this study, a Genetic Algorithm-Based Numerical Moment Matching (GA-NMM) method is adopted as a primary uncertainty estimation technique to predict the electricity consumption of a large dataset of single family homes by utilizing key features in energy audit and assessors data. This data is used as an input to the GA-NMM to develop a set of index buildings and associated weighting factors that represent statistical characteristics of the dataset. Energy models are then developed for the index buildings using physics-based energy modeling in EnergyPlus. These, in combination, are used to estimate the energy behavior of single family homes of the studied dataset. The proposed method is applied to a large dataset of 8370 single family homes in Cedar Falls, Iowa, where the expected annual and monthly electricity consumption from the model is calculated and compared with measured data. The expected site electricity consumption for single family buildings in Cedar Falls is estimated as 10,219 kWh/yr, which is within 6% of the measured average annual electricity consumption. At a monthly level, the Coefficient of Variation of Root Mean Square Error and Mean Bias Error are 7.8% and 4.5%, respectively. This method can be used to generate small set of representative homes for demonstrating the energy behavior of a larger set of homes.

Simulation of energy consumption in urban areas

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.