Affect Electricity Consumption Research Articles

Анализ влияния климатических факторов на потребление электроэнергии предприятиями в неперевозочных видах деятельности ОАО «РЖД»

Purpose: analysis of the costs of energy consumption by enterprises of JSC Russian Railways by type of fuel, identification of the most costly type of energy. Systematization of factors influencing energy consumption. Characteristics of climatic factors affecting energy consumption. The task was set to assess the possibility of using ASKUE data to improve the system for analyzing and planning energy consumption in non-transportation activities of JSC Russian Railways. Analysis of climatic factors affecting electricity consumption by enterprises in non-transportation activities of JSC Russian Railways. Determine the maximum time interval for analyzing energy consumption data. Methods: collecting information on electricity consumption and changes in climate characteristics. The main research method is the method of correlation analysis, which allows us to identify the most important factors influencing electricity consumption. Based on the correlation coefficient, a correlation matrix will be compiled using the Statistica software package. Results: climatic factors have been identified that have the greatest impact on the energy consumption of the enterprise in non-transportation activities of JSC Russian Railways. The maximum enlarged interval for analyzing energy consumption data has been clarified. Practical significance: climatic factors have been proposed, the characteristics of which must be relied upon when planning electricity consumption at non-transportation enterprises of JSC Russian Railways. These factors can be used in the further development of a model for planning energy consumption. The maximum enlarged interval for analyzing influencing factors has also been identified, allowing to obtain accurate results. The possibility of using hourly ASCAE data to assess and identify climatic factors affecting electricity consumption by non-transportation enterprises has been proven.

Potential and Challenges in Adapting Green Behavior Towards Energy-Efficient Educational Building (Case Study: i-CELL FTUI)

Abstract The amount of energy consumption in the building sector has led to the concept of green buildings. This concept is more familiar with focusing on technical aspects, where, in fact, the factor that causes the amount of energy consumption comes from the human factor. This paper aims to examine green behavior in the consumption of electrical energy in green buildings. The case study used is the FTUI i-CELL Building, which is a green building. The method used is a literature study and a questionnaire to find the potential for behavior change, savings, and challenges or obstacles from the perspective of users and managers. Based on the study’s results, the building users’ role can provide potential savings in the coverage of cooling, lighting, and electrical equipment that is greater than that of the manager. it was found that green behavior by users of the building could save 26.60%-36.55% of the energy consumption, whereas behavior by building managers only saved 8.73%-23.67%. Even so, both of them still have to work together to achieve savings in electricity consumption through green behavior. This is because the manager has an important role, such as equipment maintenance, which can affect electricity consumption. In addition, in achieving potential savings, both users and managers experience challenges or obstacles in terms of knowledge, norms, and perceptions of control that shape green behavior in electricity consumption. Suggestions for further development are adding simulation to determine the amount of savings and identify the correlation and significance of knowledge, norms, and perceptions of control that play a role in shaping green behavior.

Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features

The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals.

How does digital finance affect electricity consumption? an empirical analysis of the Yangtze River Economic Belt of China

How does digital finance affect electricity consumption? an empirical analysis of the Yangtze River Economic Belt of China

Analysis of climate-meteorological and technological factors affecting electricity consumption of mining enterprises

Electricity consumption of mining enterprises depends on many factors. The influence of factors on the process of electricity consumption is complex and diverse, and it is impossible to describe it within the framework of deterministic methods, due to the uncertainty of the conditions that determine the influence of factors. These factors include mining geological, mining technological, climate-meteorological, energetic, organizational, operational and others. Energy consumption regimes of mining enterprises depend on many mining technological factors, most of which are insufficiently taken into account in the analysis and planning of electricity consumption.

How rising temperatures affect electricity consumption and economic development in Mexico

How rising temperatures affect electricity consumption and economic development in Mexico

Determinants of Variables That Affect Electrical Energy Consumption in Indonesia 2011-2020

This study aims to analyze the determinants of variables that affect electricity consumption in Indonesia 2011-2020. There are 4 variables that are used as determinant variables, namely (1) economic growth, (2) population growth, (3) Human Development Index (HDI), and (4) internet access. This study uses a random effect model regression method using data that is panel data consisting of 34 provinces in Indonesia in the span of 2011 - 2020. Secondary data were obtained from the Central Statistics Agency (BPS), a statistical report of PT. PLN (Persero) and previous research literature studies. The results of this study found that (1) the variable of economic growth did not affect electricity consumption, (2) the population growth variable also did not affect the consumption of electricity, (3) the variable Human Development Index (HDI) had a positive effect on electricity consumption and (4) internet access variable has a negative effect on electricity consumption. Suggestions that the author can convey are: (1) optimizing the potential of New Renewable Energy (EBT), (2) conserving electricity and making efficient use of electricity, (3) increasing the electrification ratio so that electricity can be enjoyed by the entire community.

Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria

<abstract> <p>Climate change is having a significant impact on weather variables like temperature, humidity, precipitation, solar radiation, daylight duration, wind speed, etc. These weather variables are key indicators that affect electricity demand and consumption. Hence, understanding the significance of weather elements on energy needs and consumption is important to be able to adapt, strategize, and predict the effect of the changing climate on the required energy of an organization. This study aims to investigate the relationship between changing weather elements and electricity consumption, employing Multivariate Linear Regression (MLR), Support Vector Regressions (SVR), and Artificial Neural Network (ANN) models to predict the effect of weather changes on energy consumption. The following approaches were engaged for this study: Creating a catalog of weather elements and parameters of energy need or its consumption; analyzing and correlating electrical power consumption to weather factors; and developing prediction models—MLR, SVR, and ANN to predict the significance of the change in the variables of weather on the electrical energy consumption. Among the weather variables considered, temperature emerged as the most influential factor affecting electricity consumption, displaying the highest correlation. The monthly total pattern for electricity use for the case study area followed a similar pattern as the mean apparent temperature. Of the three models (MLR, SVR, and ANN) developed in this study, the ANN model yielded the best predictive performance, with Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 2.733%, 1.292%, and 4.66%, respectively. Notably, the ANN model outperformed the other models (MLR and SVR) by more than 20% across the predictive performance metrics employed.</p> </abstract>

The environmental and economic impacts of phasing out cross-subsidy in electricity prices: Evidence from China

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China.

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.

Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption.

Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China

With the rapid advancement of urbanization in China and the sharp increase in natural resources consumption, it is crucial to quantitatively study the relationship between land resources utilization and energy consumption. This paper uses the panel data at the city level from 2004 to 2019 to analyze the impact of low-density expansion of land resources on power consumption and its internal mechanism. A significant U-shaped relationship exists between urban sprawl and power consumption, which passes a variety of robustness tests. Moreover, geographical indicators are selected as instrumental variables to deal with potential endogeneity. Heterogeneity analysis shows that there are differences in the impact of urban sprawl on electricity resources for different urban locations, population sizes, sprawl characteristics, and electricity consumption types. Further mechanism analysis shows that urban sprawl affects electricity consumption by affecting variables such as real estate investment and Internet development. The conclusions of this paper bridge the conflicting viewpoints of existing research and provide empirical evidence for the utilization of land resources and the formulation of electricity regulation policies in various regions of China.

Effects of Economic Growth, Electricity Consumption, Energy Use, and Urbanisation on Carbon Dioxide Emissions in Vietnam

The aim of this paper was to explore the causal relationship between CO2 emissions, economic growth, electricity consumption, energy use, and urbanisation in Vietnam between 1982 and 2016 using the Vector Error Correction Model (VECM). In the short run, it has been empirically proven that GDP, electricity consumption, and energy use have significant and positive relationships with CO2 emissions in Vietnam. Results also indicated that electricity consumption and energy use are the main contributors to increase GDP while CO2 emissions and urban population negatively affect GDP. It was also found that GDP has a significant and negative impact on electricity consumption, but energy use positively affects electricity consumption in Vietnam. Further, urban population has a negative influence on energy use, while the latter has a positive relationship with urban population. In the long run, results showed that GDP per capita and energy use per capita have significant and positive influence on CO2 emissions in Vietnam while urban population contributes to reducing CO2 emissions. The results of the Johansen co-integration test showed that there is a long run relationship between CO2 emissions, economic growth, electricity consumption, energy use, and urbanisation. Results implied that economic growth should be considered by the Vietnamese government along with the target in environmental protection, and urbanisation should be carefully managed to achieve sustainable development in Vietnam.

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.

Decoupling of Electricity Consumption Efficiency, Environmental Degradation and Economic Growth: An Empirical Analysis

The present study investigates electricity consumption, carbon dioxide (CO2) emission, and economic growth decoupling using data from 1971 to 2020 for the economy of China. The study uses decoupling analysis (DA) as the prime methodology for analysis. Furthermore, the findings put forward a significant contribution to an economic picture of the economy of China and a sizeable addition to related research and findings under the assigned issues discussed in the study. The study’s main contribution is to decouple electricity consumption from the gross domestic product (GDP), which is rare in the existing literature in the context of China. Moreover, the study shows the decoupling of environment affects electricity consumption, and GDP growth. The DA model shows that electricity consumption is the main driving force enhancing economic growth. However, industrialization has increased greenhouse gases, global warming, and climate change due to production and consumption. China’s economy uses coal for energy resources, which indicates that China produces a large proportion of electricity with coal, which causes high CO2 emissions. Finally, further analysis with the Granger causality test confirms the main findings.

Estimation of the price and income elasticities of residential electricity demand in Indonesia using household-level data

This paper presents household-level econometric estimates of the income and price elasticities of residential electricity demand for Indonesia. Using annual household survey panel data of SUSENAS from 2011 to 2013, the estimation controls for household characteristics that significantly affect electricity consumption, such as demographic aspects, house size, and ownership of home appliances. The empirical results show that in Indonesia, the residential electricity is price- and income-inelastic, with price and income elasticities of -0.88 and 0.3, respectively. Urban residents use more electricity than their rural counterparts. Responding to the growing economy, the government should prepare a greater electricity capacity or induce a higher tariff to promote electricity savings.

Automatic Selection of Temperature Variables for Short-Term Load Forecasting

Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhouse gas emissions. Temperature affects electricity consumption through air conditioning and heating equipment, although it is the consumer’s behavior that determines specifically to what extent. This work proposes an automatic method of processing and selecting variables, with a two-fold objective: improving both the accuracy and the interpretability of the overall forecasting system. The procedure has been tested by the predictive system of the Spanish electricity operator (Red Eléctrica de España) with regard to peninsular demand. During the test period, the forecasting error was consistently reduced for the forecasting horizon, with an improvement of 0.16% in MAPE and 59.71 MWh in RMSE. The new way of working with temperatures is interpretable, since they separate the effect of temperature according to location and time. It has been observed that heat has a greater influence than the cold. In addition, on hot days, the temperature of the second previous day has a greater influence than the previous one, while the opposite occurs on cold days.

Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures

The high energy density of university dormitories deserves attention for its huge energy-saving potential. To investigate the patterns and influencing factors of electricity consumption in university dormitories, this study presents a methodology that combines clustering and covariance method based on balancing point temperatures. The dataset of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The main results are as follows. (1) Using the Joinpoint Regression (JPR) method, two balance point temperatures, 20.1 °C and 26.6 °C, that affect electricity consumption trend are identified. (2) Through cluster analysis of 3D data, the dormitories are grouped into three electricity use patterns. The phenomenon of the shift in electricity consumption patterns from the fall semester to the spring semester is discovered. (3) The impact of 8 non-behavioral factors on electricity consumption is investigated through covariance analysis. The results show that gender is the most influential factor in base electricity consumption, while three factors have the greatest impact on cooling electricity consumption: floor, occupancy and room location. (4) Based on the main influential factors of room cooling load, a hot season daily cooling electricity subsidy strategy is proposed to balance the difference in cooling electricity bills for different rooms. This study provides reliable information to support the design of potential electricity saving and management measures for dormitory buildings.

How do the industrial structure and international trade affect electricity consumption? New evidence from China

Since the reform and opening up, electricity consumption in China has grown rapidly. However, the energy and environmental problems partly caused by the power generation structure, which is dominated by coal-fired power generation, have deepened, affecting the orderly and sustainable development of electricity. This paper investigates the relationship among electricity consumption, economic output, the industrial structure and international trade at the national and regional levels using 1999–2020 panel data covering 30 Chinese provinces and employing a panel vector autoregression (PVAR) model and quantile regression. The empirical results show that electricity consumption and GDP are mutually reinforcing in the full sample and in the eastern region. Exports have a significant impact on electricity consumption in the short term. A 1% increase in exports in the current period leads to a corresponding increase in electricity consumption of approximately 0.04% in the following period. In the long run, the response of electricity consumption to imports is positive. Establishing a quantity-based renewable energy certificate system and optimizing the export commodity structure in the central region may be effective ways to solve China's current electricity consumption problem.

Raylı Sistem Araçlarında Enerji Verimliliğinin İyileştirilmesi

Over the years, due to traffic, environmental problems associated with fossil fuels and related economic problems, the demand for urban rail transit system such as trams, light rail systems and metro has increasing rapidly not only in our country but also all over the world. Since rail public transportation systems require high power, the energy efficiency of the railway system is of great importance. The energy consumption on tram lines is different. Therefore, the factors affecting electricity consumption of trams and metro, especially in metropolitan cities should be examined carefully and the new methods for enhancing energy efficiency of the rail transportation systems should be developed. In this study, tests including different driving styles were carried out for 2 CNR railway vehicles owned by SAMULAŞ A.Ş., which serve in the urban public transportation system in Samsun. The test results were analyzed in terms of traction power and relationship between energy consumption and vehicle weight using the Concerto Maintenance program. The simulation results showed that 20% energy saving was achieved by using the suggested driving scenarios, compared to practical driving at similar vehicle weight. For example, an average of 14 million kWh of energy is consumed annually for rail public transportation systems in Samsun. For this reason, it is thought that the findings obtained in this study will contribute to the reduction of energy consumption of urban rail systems in Turkey.