Urban Electricity Consumption Research Articles

An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh.

Temperature variation and urban electricity consumption in China: Implications for demand management and planning

With the pressing challenges of climate change and rapid urbanization, understanding the impact of temperature variation on electricity consumption in urban areas is crucial for effective demand management and planning. This study investigates the relationship between temperature and electricity usage in Chinese urban centers, shedding light on critical patterns that can inform sustainable energy policies. Using panel data analysis reveals a significant impact of temperature change on household electricity consumption. The findings indicate an optimal temperature range of 18–24 °C and a non-linear association between temperature and power usage. Notably, the rise in summer temperatures has a greater impact on electricity consumption than the decrease in colder winters. The study also highlights variations in the influence of temperature on electricity consumption based on regional economic development and north-south disparities. The study suggests implications for optimizing power supply planning.

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

The state of renewable energy source envelopes in urban areas

Several computational algorithms have been developed to determine building heating and cooling energy requirements on a citywide scale. However, limited contemporary models can analyse all structures in an urban region, and even fewer can handle power consumption on monthly basis or at lower levels. Nevertheless, these characteristics are crucial for urban power generation planners. In light of the imperative role that accurate modelling plays in evaluating urban energy systems, this paper underscores the critical need to overcome the substantial processing costs associated with existing models, recognizing that addressing this challenge is pivotal for precise simulation of urban electricity consumption and, consequently, for informing effective urban power generation planning strategies. This study delves into the integration of solutions related to renewable energy source envelopes, focusing particularly on the strategic approach within clusters of buildings in urban energy systems. The primary objective of this research is to elucidate the cluster methodology and its defining parameters, followed by an exploration of key influencing factors and modelling methodologies. The study is delimited by specific considerations encompassing barrier dimensions, methodological intricacies, and the pivotal elements that govern renewable energy source envelope-based power provisions for building clusters. According to the conducted analysis, the processing costs of these models are a significant hurdle to accurate microsimulation of urban electricity consumption. Theoretical and numerical approximations should be used; even computational methodologies are subject to uncertainty analysis and error measurement.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Urban Electricity Consumption Forecasting Based on SARIMA and Random Forest Modeling

Urban Electricity Consumption Forecasting Based on SARIMA and Random Forest Modeling

The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China

Under the urban development trend of sprawl, improving energy use efficiency is a proper way to promote green and low-carbon construction in cities. This paper uses panel data from 283 prefecture-level and above cities in China from 2008 to 2019 to measure the urban sprawl index, and analyze the spatial-temporal evolution law of urban sprawl and electricity consumption. The relationship between urban sprawl and electricity consumption is empirically examined, and the differential effect of urban sprawl on electricity consumption is analyzed. Finally, the impact of urban sprawl on electricity consumption based on a spatial perspective is explored in depth by establishing a spatial error model. We found the following: (1) The levels of urban sprawl and urban electricity consumption are on the rise. The spatial distribution of urban sprawl is more dispersed, and cities with high electricity consumption levels are mostly concentrated in the eastern coastal areas. (2) Urban sprawl exacerbates electricity consumption, and this conclusion is still robust after a series of robustness tests were conducted and endogeneity issues were taken into account. In terms of the influence mechanism, urban sprawl mainly affects electricity consumption by changing the allocation of land resources, increasing the dependence on private transportation, and inhibiting green technology innovation. (3) The incremental effect of urban sprawl on electricity consumption is more pronounced in cities with high sprawl levels, weak environmental regulations, and low green innovation levels, as well as in west cities. (4) Urban sprawl and electricity consumption both have a significant positive spatial correlation. Electricity consumption of cities is not only related to their own regions but also influenced by the adjacent regions, and the spatial correlation is mainly reflected in the random error term. This paper deepens the understanding of the basic laws of urban sprawl affecting urban low-carbon development, which also has implications for new urbanization strategies and green development.

Recent trends in urban electricity consumption for cooling in West and Central African countries

Recent trends in urban electricity consumption for cooling in West and Central African countries

How climate change affects electricity consumption in Chinese cities-a differential perspective based on municipal monthly panel data.

Addressing the impacts of climate change has become a global public crisis and challenge. China is characterized by a complex and diverse topography and vast territory, which makes it worthwhile to explore the differential impacts of climate change on urban electricity consumption in different zones and economic development conditions. This study examines the differential impact of climate factors on urban electricity consumption in China based on monthly panel data for 282 prefectures from 2011 to 2019 and projects the potential demand for future urban electricity consumption under different climate change scenarios. The results show that (1) temperature changes significantly alter urban electricity consumption, with cooling degree days (CDD) and heating degree days (HDD) contributing positively to urban electricity consumption in areas with different regional and economic development statuses, with elasticity coefficients of 0.1015-0.1525 and 0.0029-0.0077, respectively. (2) The temperature-electricity relationship curve shows an irregular U-shape. Each additional day of extreme weather above 30 °C and below -12 °C increases urban electricity consumption by 0.52% and 1.52% in the north and by 2.67% and 1.32% in the south. Poor cities are significantly more sensitive to extremely low temperatures than rich cities. (3) Suppose the impacts of climate degradation on urban electricity consumption are not halted. In that case, the possible Shared Socioeconomic Pathways 1-1.9 (SSP1-1.9), SSP1-2.6, and SSP2-4.5 will increase China's urban electricity consumption by 1621.96 billion kWh, 2960.87 billion kWh, and 6145.65 billion kWh, respectively, by 2090. Finally, this study makes some policy recommendations and expectations for follow-up studies.

Prediction and comparison of urban electricity consumption based on grey system theory: A case study of 30 southern China cities

Rapid urbanization has put heavy pressure on urban energy supply over the past decades in China and such a trend is expected to continue. Therefore, it is critical to capture the impacts of key factors on urban energy consumption and further predict its future growth. This study proposes a framework to predict urban-scale electricity consumption which incorporates an original urban information database with 18 macroeconomic factors and 2 electricity consumption indicators. The grey prediction model is the core for prediction, whose performance is investigated in the established database. Meanwhile, multiple strategies, e.g., variable screening and optimal data allocation, have been conducted to improve the accuracy of the prediction. A case study of 30 cities in southern China indicates that the established database can well represent the relation between urban development and energy consumption. The proposed prediction framework performs well in forecasting urban-scale electricity consumption with less than 10 % of mean absolute percentage error, showing a positive correlation between model accuracy and data amount. Overall, the proposed prediction framework generates valuable insights into the interaction between urban development and electricity consumption, which can quantitatively inform urban designers when planning urban energy systems.

High-Resolution Carbon Accounting Framework for Urban Water Supply Systems.

Decarbonization of urban infrastructure systems is imperative to meeting global climate goals. Urban water supply systems (UWSSs) account for 1-3% of urban electricity consumption in the U.S., a value expected to increase, as municipalities tap nontraditional water supplies that are either more distant or require more energy-intensive treatment. Reducing the carbon intensity of UWSSs will require a combination of infrastructure upgrades, operational modifications, and behavioral interventions, but urban water planners, water treatment system operators, and consumers lack transparent tools for quantifying the carbon emission implications of these decisions. We propose a high-resolution carbon accounting framework that allows for attribution of carbon emissions to individual water sources, water system components, or individual consumers in a UWSS. The high temporal resolution of this framework also enables rapid assessment of the potential for operational and behavioral interventions to reduce the carbon intensity of UWSSs. We demonstrate this carbon accounting framework on a real-world UWSS serving a city of roughly 100 000 residents. The high spatial and temporal resolution, coupled with the scalability of this approach, makes it a valuable tool for consulting engineers, operators, and consumers seeking to deliver Net Zero water supplies.

Research on the synergies between low-carbon pilot city policy and high-speed railways in improving Chinese urban electricity efficiency

PurposeGlobal climate change is a serious threat to the survival and development of mankind. Reducing carbon emissions and achieving carbon neutrality are the keys to reducing greenhouse gas emissions and promoting sustainable human development. For many countries, taking China as an example, the electric power sector is the main contributor to the country’s carbon emissions, as well as a key sector for reducing carbon emissions and achieving carbon neutrality. The low-carbon transition of the power sector is of great significance to the long-term low-carbon development of the economy. Therefore, on the one hand, it is necessary to improve the energy supply structure on the supply side and increase the proportion of new energy in the total power supply. On the other hand, it is necessary to improve energy utilization efficiency on the demand side and control the total primary energy consumption by improving energy efficiency, which is the most direct and effective way to reduce emissions. Improving the utilization efficiency of electric energy and realizing the low-carbon transition of the electric power industry requires synergies between the government and the market. The purpose of this study is to investigate the individual and synergistic effects of China’s low-carbon policy and the opening of urban high-speed railways (HSRs) on the urban electricity consumption efficiency, measured as electricity consumption per unit of gross domestic product (GDP).Design/methodology/approachThis study uses a panel of 289 Chinese prefecture-level cities from the years 1999–2019 as the sample and uses the time-varying difference-in-difference method to test the relationship between HSR, low-carbon pilot cities and urban electricity consumption efficiency. In addition, the instrumental variable method is adopted to make a robustness check.FindingsEmpirical results show that the low-carbon pilot policy and the HSR operation in cities would reduce the energy consumption per unit of GDP, and synergies occur in both HSR operated and low-carbon pilot cities.Research limitations/implicationsThis study has limitations that would provide possible starting points for future studies. The first limitation is the choice of the proxy variable of government and market factors. The second limitation is that the existing data is only about whether the high-speed rail is opened or not and whether it is a low-carbon pilot city, and there is no more informative data to combine the two aspects.Practical implicationsThe findings of this study can inform policymakers and regulators about the effects of low-carbon pilot city policies. In addition, the government should consider market-level factors in addition to policy factors. Only by combining various influencing factors can the efficient use of energy be more effectively achieved so as to achieve the goal of carbon neutrality.Social implicationsFrom the social perspective, the findings indicate that improving energy utilization is dependent on the joint efforts of the government and market.Originality/valueThe study provides quantitative evidence to assess the synergic effect between government and the market in the low-carbon transition of the electric power industry. Particularly, to the best of the authors’ knowledge, it is the first to comprehend the role of the city low-carbon pilot policy and the construction of HSR in improving electricity efficiency.

Recent Trends in Urban Electricity Consumption for Cooling in West and Central African Countries

Recent Trends in Urban Electricity Consumption for Cooling in West and Central African Countries

Multi-objective optimization and exergoeconomic evaluation of a hybrid geothermal-PVT system integrated with PCM

The main purpose of this research is to study the techno-economic analysis of a photovoltaic thermal collector (PVT) combined with phase change materials (PCM) and ground source heat pump (GSHP). In the first step of this study, a numerical transient simulation of a water-to-water GSHP was developed by using a vertical U-type ground source heat exchanger (GSHX) and variable speed drive (VSD) compressor in the multi-usage operation modes (heating and cooling). To achieve the most supreme system performance, the multi-objective optimization of the Levelized cost of energy (LCOE) and exergy efficiency was done by using the multi-objective non-dominated sorting genetic algorithm version || (NSGA-||). This algorithm considers substantial elements such as the number of boreholes, depth of drilling for the location of pipes in GSHX, setpoint temperature of fan coil, length of converters, the number of PVT panels, slope and direction of PVT panels, the number of batteries, volume of the storage tank of phase change materials and percentage of phase change materials in the tank. The simulation of the heat pump is accomplished through a numeral coding in the Engineering Equation Solver (EES) and also for the optimization part, a programming method used in the MATLAB program. Moreover, due to the evaluation of the transient response of the model, TRNSYS software was implemented. Based on the research achievements, the Single objective optimization of exergy efficiency for the combined PVT-PCM and GSHP systems can be conducted to the highest solar factor (SF) and the lowest amount of urban electricity consumption. Furthermore, the calculation of the annual required load of the building for different scenarios has shown that the use of collectors in this combined system has reduced the total load of the building by 6.5%. The maximum percentage of PVT energy efficiency belongs to the first scenario with a value of 53% and the lowest one with a difference of 43% is for the fourth scenario.

Residential Electricity Consumers and Increasing Block Pricing Policy in Pakistan: Evidence Based on Household Level Primary Data

The purpose of this paper is to explore the efficacy of increasing block electricity pricing (IBEP) which was imposed in 2013 by the Pakistani government. The main objective of this policy is rational resource allocation and to lower the cross-subsidization to the residential sector by two other major sectors such as commerce and industry. The study is based on primary data which is collected through questionnaires from four tehsils of district Sargodha in rural as well as the urban regions. About 54.6% of households understand the electricity price scheme (IBEP) while urban users are more aware as compared to rural but urban electricity consumption is higher due to high use of home appliances. By using the regression model the elasticity of residential electricity consumption is estimated for heterogeneous consumers. The upper blocks of electricity consumption are more sensitive to price increases (e.g., the elasticity of the first to fourth block is -0.391, -0.988, -1.229, -0.955 respectively) except in the fifth block (-0.489) which indicates that higher income group pays more attention to the standard of living. The increase in the number of trees also affects the price elasticity of residential electricity use and positively influences saving behaviour. The policy which was implemented from a welfare point of view has achieved its target to a certain extent. Future guidelines for the up-gradation of electricity pricing reform in the residential sector are thus proposed.

Critical Determinants of Household Electricity Consumption in a Rapidly Growing City

Despite growing urban electricity consumption, information on actual energy use in the household sector is still limited and causal factors leading to electricity consumption remain speculative due to urban expansion and its growing complexity, particularly in developing countries such as Malaysia. This study aims to examine the critical determinants of household electricity consumption by evaluating the patterns and flows of consumption and analysing relationships and their effects on electricity usage among 620 urban households in Seremban, Malaysia. Results suggest that the average urban household electricity consumption is 648.31 kWh/month; this value continues to grow with the increase in the household monthly income (r = 0.360; p < 0.01) and number of rooms (r = 0.360; p < 0.01) as quality of life improves. A large portion of electricity is allocated for kitchen/home consumption, followed by cooling and lighting. Multiple linear regressions revealed that married households with a high monthly income and living in spacious houses together with three to five people are important predictors of electricity consumption in Seremban. This study empirically identified that the number of rooms is the most critical factor of electricity consumption and strategies to increase energy efficiency, maintain resource sustainability and minimise greenhouse gas threat on the urban ecosystem are vital. Therefore, promoting low carbon initiatives for energy conservation and technology improvement and implementing policies in the domestic sector are essential to achieve the greatest potential energy consumption reduction in urban regions.

Estimation of domestic urban electricity consumption: A case study of Baghdad, Iraq

Electricity consumption for household purposes in urban areas widely affects the general urban consumption compared to other commercial and industrial uses, as household electricity consumption is affected by many factors related to the physical aspects of the residential area such as temperature, housing unit area, and coverage ratio, as well as social and economic factors such as family size and income, to reach the extent of the influence of each of the above factors on the amount of electricity consumed for residential uses, a selected sample of a residential area in the city of Baghdad was studied and a field survey conducted of the characteristics of that sample and the results analyzed and modeled statistically in relation to the amount of electricity consumed for residential units. Results showed that the annual electricity consumption is directly proportional to both the increase in the residential plot area and the increase in the number of residents of the housing unit and the increase in the monthly income of households, while it is inversely proportional to the plot coverage rate. Through the results of the field research, it was possible to build six quantitative models describing the behavior of electricity consumption in relation to the variables covered in the research.

Estimation of domestic urban electricity consumption: A case study of Baghdad, Iraq

Electricity consumption for household purposes in urban areas widely affects the general urban consumption compared to other commercial and industrial uses, as household electricity consumption is affected by many factors related to the physical aspects of the residential area such as temperature, housing unit area, and coverage ratio, as well as social and economic factors such as family size and income, to reach the extent of the influence of each of the above factors on the amount of electricity consumed for residential uses, a selected sample of a residential area in the city of Baghdad was studied and a field survey conducted of the characteristics of that sample and the results analyzed and modeled statistically in relation to the amount of electricity consumed for residential units. Results showed that the annual electricity consumption is directly proportional to both the increase in the residential plot area and the increase in the number of residents of the housing unit and the increase in the monthly income of households, while it is inversely proportional to the plot coverage rate. Through the results of the field research, it was possible to build six quantitative models describing the behavior of electricity consumption in relation to the variables covered in the research.

Nonlinear Effect Analysis of Electricity Price on Household Electricity Consumption

The household energy consumption has been a hot field in the study of household energy consumption in recent years. With the increase of residents’ income level and the pushing of urbanization, there is a complex nonlinear relationship between energy price and energy consumption. The purpose of this paper is to investigate the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. To this direction, based on the regional characteristics of economic development in China, with the residents’ disposable income and the urbanization level as the conversion variables and the electricity sales price as the core explanatory variable, the panel smooth transition regression (PSTR) model of electricity sales price and urban household electricity consumption from the perspectives of income level and urbanization has been constructed in this paper. The empirical results show the following: (1) Under the consideration of regional difference of residents’ income level, with the increase of residents’ disposable income level, there is a significant negative correlation between electricity sales price and urban household electricity consumption in the whole country, the eastern region, and the central region, while such correlation is significantly positive in the western region. (2) Under the consideration of the difference of urbanization development level, the national regional electricity sales price and the urbanization level are positively related to the urban household electricity consumption, and the urbanization level in the western region plays the biggest role in promoting the urban household electricity consumption, followed by the eastern region and then the central region which plays the smallest role. This paper discusses the effect of electricity sales price on urban household electricity consumption from the perspective of regional difference in income and urbanization, which provides the decision-making basis and empirical support for developing regional electricity price policy and household energy consumption policy.

Analysis on the trend and characteristics of electricity consumption growth in Shaoxing

This article studies the current situation and development trend of electricity consumption in Shaoxing from the perspectives of electricity consumption in the whole society, industrial electricity consumption, and urban and rural residents' daily life electricity consumption. Compared with the average electricity consumption of the whole country, Zhejiang Province and various regions, we analyze power consumption trends and structural characteristics of Shaoxing