Статті в журналах: "Orecasting of electricity consumption"

1

Stoy, Christian, and Susanne Kytzia. "Benchmarking electricity consumption." Construction Management and Economics 24, no. 10 (October 2006): 1083–89. http://dx.doi.org/10.1080/01446190600799414.

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2

Qiu, Yueming, Bo Xing, and Yi David Wang. "PREPAID ELECTRICITY PLAN AND ELECTRICITY CONSUMPTION BEHAVIOR." Contemporary Economic Policy 35, no. 1 (February 22, 2016): 125–42. http://dx.doi.org/10.1111/coep.12170.

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3

M, Sathish Kumar, Sasidharan K, and Sridar G.Srinivas. "Electricity Consumption Notification Application." International Innovative Research Journal of Engineering and Technology 2, no. 1 (September 30, 2016): 1–2. http://dx.doi.org/10.32595/iirjet.org/v2i1.2016.26.

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4

Akasiadis, Charilaos, and Georgios Chalkiadakis. "Cooperative electricity consumption shifting." Sustainable Energy, Grids and Networks 9 (March 2017): 38–58. http://dx.doi.org/10.1016/j.segan.2016.12.002.

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5

Andersen, Frits M., Morten S. Christensen, Ole Michael Jensen, Niels-Ulrik Kofoed, and Poul Erik Morthorst. "Second-home electricity consumption." Energy Policy 36, no. 1 (January 2008): 280–89. http://dx.doi.org/10.1016/j.enpol.2007.09.013.

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6

Ringwood, J. V., P. C. Austin, and W. Monteith. "Forecasting weekly electricity consumption." Energy Economics 15, no. 4 (October 1993): 285–96. http://dx.doi.org/10.1016/0140-9883(93)90018-m.

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7

Chirwa, Themba G., and Nicholas M. Odhiambo. "Electricity consumption and economic growth." International Journal of Energy Sector Management 14, no. 1 (January 6, 2020): 1–19. http://dx.doi.org/10.1108/ijesm-11-2018-0014.

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Анотація:

Purpose This paper aims to examine the short- and long-run relationship between electricity consumption and economic growth. Design/methodology/approach The study uses a panel-based autoregressive distributed lag approach to cointegration to investigate this relationship in 12 advanced, emerging markets and developing economies during the period 1970-2016, selected from three continents, namely, Europe (Luxemborg, Norway, Denmark and Belgium), Asia (Singapore, Japan, Indonesia and India) and Africa (South Africa, Algeria, Egypt and Kenya). Findings Based on the homogeneity assumption, the study results reveal that electricity consumption is positively and significantly associated with economic growth in all the study countries in the long run. Conversely, the short-run results reveal that electricity consumption is positively and significantly associated with economic growth in ten countries and negatively associated with economic growth in only two countries. Research limitations/implications The study concludes that, on the whole, electricity consumption is an important factor of production in the majority of the study countries. Therefore, policymakers should focus on growth-enhancing energy policies that promote energy efficiency usage, especially in the long run. Originality/value The authors hereby confirm that the paper has not been published elsewhere, and this research is entirely their work.

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8

CRAWSHAW, A. J. E., D. I. WILLIAMS, and C. M. CRAWSHAW. "Consumer knowledge and electricity consumption." Journal of Consumer Studies and Home Economics 9, no. 4 (December 1985): 283–89. http://dx.doi.org/10.1111/j.1470-6431.1985.tb00099.x.

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9

Pradhan, Gyanendra Lal. "Electricity: Domestic Consumption Versus Export." Hydro Nepal: Journal of Water, Energy and Environment 3 (May 26, 2009): 16–18. http://dx.doi.org/10.3126/hn.v3i0.1913.

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Due to its unique geography, Nepal is gifted with very high hydropower potential, far greater than generally accepted figure of 83,000 MW and 43000 MW of theoretical and techno-financial viability. Our neighbors India, Pakistan and Bangladesh are suffering from huge power shortages. There is no point in debating whether Nepal’s hydropower should be for domestic consumption or for export because the potential of generating hydropower in Nepal is far greater than its domestic consumption, even in 2050. Due to the lack of appropriate policies, however, Nepal suffers from long hours of load shedding. The government policy to subsidize petroleum products was a big mistake for, like Bhutan, electricity should have been the cheapest source of energy. Politicians are focusing mostly in export oriented projects; whereas, higher importance should have been given to projects for domestic consumption. We should aim at producing twice the needed internal demand. Nepal is poised to reap huge benefits from hydropower.Key words: Hydropower; domestic consumption; load shedding; export; Nepaldoi: 10.3126/hn.v3i0.1913Hydro Nepal Journal of Water, Energy and EnvironmentIssue No. 3, July 2008. Page: 16-18

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10

Fullerton, Thomas M., David A. Juarez, and Adam G. Walke. "Residential electricity consumption in Seattle." Energy Economics 34, no. 5 (September 2012): 1693–99. http://dx.doi.org/10.1016/j.eneco.2012.02.004.

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11

Kräuchi, P., C. Dahinden, D. Jurt, V. Wouters, U. P. Menti, and O. Steiger. "Electricity consumption of building automation." Energy Procedia 122 (September 2017): 295–300. http://dx.doi.org/10.1016/j.egypro.2017.07.325.

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12

Berg, Sanford V., and Christopher Taylor. "Electricity consumption in manufactured housing." Energy Economics 16, no. 1 (January 1994): 54–62. http://dx.doi.org/10.1016/0140-9883(94)90018-3.

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13

Cui, Chang-Ri, Jeong-Taek Kim, Sehee Kim, Woo-Jong Lee, and Hee-Yeon Sunwoo. "Green commitment and electricity consumption." Economics Letters 214 (May 2022): 110452. http://dx.doi.org/10.1016/j.econlet.2022.110452.

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14

Zhang, Lianwei, and Xiaoni Wen. "Nonlinear Effect Analysis of Electricity Price on Household Electricity Consumption." Mathematical Problems in Engineering 2021 (April 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/8503158.

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Анотація:

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.

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15

Agbaje, Busrat Abidemi, and Ekele Idachaba. "Electricity Consumption, Corruption and Economic Growth." International Journal for Innovation Education and Research 6, no. 4 (April 30, 2018): 193–214. http://dx.doi.org/10.31686/ijier.vol6.iss4.1023.

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Анотація:

An important prerequisite for reducing poverty, sustainable development and achievement of the millennium development goal has to some extent been tied to access to electricity. However, the subject matter; 'electricity consumption causing economic growth' has seen conflicting results from the theoretical and empirical front, if indeed a relationship exist at all. The study tests, within a panel context the long-run relationship between electricity consumption and economic growth for 13 African Countries from 2006 to 2017 by employing recently developed panel co-integration techniques. Implementing a three stage approach made up of panel unit root, panel co-integration and Granger causality test to examine the causal relationship between electricity consumption, electricity price, corruption, employment and growth. The study provides empirical evidence that a bidirectional causal relationship between electricity consumption and economic growth exist in the short run, suggesting that lack of electricity could hamper economic growth as well as an investment in electricity infrastructure would in turn improve economic growth. Also reveals that corruption causes the level of electricity consumption and GDP in the short run. On the long-run front electricity consumption and electricity price granger causes GDP and GDP causes electricity consumption.

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16

Adi, Tri Wahyu, Eri Prabowo, and Oetami Prasadjaningsih. "Influence of Electricity Consumption of Industrial and Business, Electricity Price, Inflation and Interest Rate on GDP and Investments in Indonesia." International Journal of Energy Economics and Policy 12, no. 3 (May 18, 2022): 331–40. http://dx.doi.org/10.32479/ijeep.13022.

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This study aims to explore the influence of electricity consumption, electricity price, inflation and interest rate on GDP and investments in Indonesia in the period 2001-2018. This paper is explanatory research. A Generalized Structured Component Analysis was a component-based approach to Structural Equation Modelling has used as a research model. The empirical analysis uses time-series data of GDP, Electricity Consumption, Electricity Price, Inflation Rate, Interest Rate, Investments and GDP in Indonesia in the period 2001-2018. The findings of this study are electricity consumption has a significant positive effect on GDP and electricity price. Electricity price has an insignificant positive effect on electricity consumption and investment. GDP has a significant positive effect on electricity consumption but insignificant on investment and inflation. Investment has an insignificant negative effect on electricity consumption and inflation. Inflation has a significant positive effect on the interest rate, vice versa, but is insignificant to electricity consumption. The interest rate has an insignificant positive effect on investment. The Originality of this study, namely previous studies focused more on the relationships and causality between Electricity Consumption, FDI, GDP, while in this study the emphasis is more on predictions between latent variables using the GSCA. In previous studies using total electricity consumption, in this study, the latent variable of electricity consumption is formed by industry electricity consumption and business electricity consumption which is productive consumption in increasing GDP. This study uses a multi-variate study consisting of Electricity Consumption of Industrial and Business, Electricity Price, Investment, GDP variables, and adding Inflation Rate and Interest Rate that represent macro-economic conditions in the research model.

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17

Wallis, Hannah, Malte Nachreiner, and Ellen Matthies. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households." Energy Policy 94 (July 2016): 224–34. http://dx.doi.org/10.1016/j.enpol.2016.03.046.

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18

Huang, Yunyou, Jianfeng Zhan, Chunjie Luo, Lei Wang, Nana Wang, Daoyi Zheng, Fanda Fan, and Rui Ren. "An electricity consumption model for synthesizing scalable electricity load curves." Energy 169 (February 2019): 674–83. http://dx.doi.org/10.1016/j.energy.2018.12.050.

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19

Wang, Bo, Ziyue Yuan, Xiangxiang Liu, Yefei Sun, Bin Zhang, and Zhaohua Wang. "Electricity price and habits: Which would affect household electricity consumption?" Energy and Buildings 240 (June 2021): 110888. http://dx.doi.org/10.1016/j.enbuild.2021.110888.

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20

Gaoqi, Dai, Lin Haifeng, Zhang Yongjian, and Xia Liyu. "Analysis on the trend and characteristics of electricity consumption growth in Shaoxing." E3S Web of Conferences 236 (2021): 01003. http://dx.doi.org/10.1051/e3sconf/202123601003.

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Анотація:

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

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21

Liyu, Xia, and He Wan. "Long-term Relationship between Electricity Consumption and Economic Growth." E3S Web of Conferences 165 (2020): 06014. http://dx.doi.org/10.1051/e3sconf/202016506014.

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Electricity is an indispensable material basis for economic development. It is necessary to study the relationship between different electricity consumption and economic growth. Based on the quarterly data of China’s electricity consumption and economic development from 2011 to 2018, the long-term equilibrium relationship between variables are analyzed from a causal perspective, and electricity consumption indicators for reflecting economic development are identified. The results show that there is a long-term equilibrium relationship between secondary industry electricity consumption, industrial electricity consumption and GDP. The demand for electricity consumption still needs to be met urgently.

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22

Ogorodnikov, Nikita. "Electricity demand management within the retail electricity market in Russia." E3S Web of Conferences 289 (2021): 01006. http://dx.doi.org/10.1051/e3sconf/202128901006.

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Demand side management is an important tool for ensuring the flexibility of the electric power systems, as it maintains and regulates the balance of generation and consumption of electric energy and has a system-wide effect, which is formed by lowering electricity prices for consumers and optimizing the load and structure of generating and electric grid capacities. The article is devoted to the problem of managing the demand for electricity consumption within the framework of the retail electricity market in Russia. The author identifies and summarizes the existing demand management tools. The article substantiates the need for the development and implementation of innovative tools and mechanisms for managing the demand for electricity consumption.

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23

N.S., Ogorodnikov. "Electricity demand management within the retail electricity market in Russia." E3S Web of Conferences 209 (2020): 03021. http://dx.doi.org/10.1051/e3sconf/202020903021.

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Анотація:

The article is devoted to the problem of managing the demand for electricity consumption within the framework of the retail electricity market in Russia. The author identifies and summarizes the existing demand management tools. The article substantiates the need for the development and implementation of innovative tools and mechanisms for managing the demand for electricity consumption.

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24

Shibano, Kyohei, and Gento Mogi. "Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania." Energies 13, no. 10 (May 15, 2020): 2497. http://dx.doi.org/10.3390/en13102497.

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When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.

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25

Wang, Hai Yan, and Shi Jun Chen. "Study on Forecasting Model of Monthly Electricity Consumption Based on Kernel Partial Least-Squares and Exponential Smoothing Method." Advanced Materials Research 805-806 (September 2013): 1221–27. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1221.

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it is very necessary for electricity market operation to accurate forecasting monthly electricity consumption, influencing factors of electricity consumption, there are non-linear and strong correlation, taking into account the cyclical trend of the monthly electricity consumption, this paper raises a monthly electricity consumption forecast model based on kernel partial least squares and exponential smoothing regression. The forecast model is the first to use kernel partial least squares regression methods to predict the annual electricity consumption, and then combined with exponential smoothing obtained monthly electricity accounts for the proportion of electricity consumption throughout the year for each month of the year to be measured power consumption . Instance analysis and calculation results show that the method has higher prediction accuracy, good practicality and feasibility.

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26

Amber, Khuram, Rizwan Ahmad, Mina Farmanbar, Muhammad Bashir, Sajid Mehmood, Muhammad Khan, and Muhammad Saeed. "Unlocking Household Electricity Consumption in Pakistan." Buildings 11, no. 11 (November 22, 2021): 566. http://dx.doi.org/10.3390/buildings11110566.

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In Pakistan, data for household electricity consumption are available in the form of monthly electricity bills only, and, therefore, are not helpful in establishing appliance-wise consumption. Further, it does not help in establishing the relationship among the household electricity consumption and various driving factors. This study aimed to unlock the household electricity consumption in Pakistan by analyzing electricity bills and investigating the impact of various socioeconomic, demographic, and dwelling parameters and usage of different appliances. The methodology adopted in this study was survey-based data collection of the residential sector. For this purpose, data were collected from 523 dwellings through surveys and interviews in Mirpur city. The results of the data analysis revealed that the average household electricity consumption is 2469 kWh/year with an average family size of seven and an average floor area of 78.91 m2. Based on possession of various appliances, the households were categorized into four types and their consumption patterns were established and compared. Air Conditioned (AC) houses consume 44% more electricity compared to the non-AC houses, whereas an Uninterrupted Power Supply (UPS) consumes electricity equivalent to an AC. The research findings are useful for policy makers and building designers and are discussed in the conclusion section.

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27

Bao, Chao, and Ruowen Liu. "Electricity Consumption Changes across China’s Provinces Using A Spatial Shift-Share Decomposition Model." Sustainability 11, no. 9 (April 28, 2019): 2494. http://dx.doi.org/10.3390/su11092494.

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China’s growing electricity consumption has become an important factor to improving socio-economic development, as well as aggravating environmental degradation. Based on the provincial level data in China for the entire period and every five years during 2000–2015, this paper used a spatial shift-share analysis (SSS) to detect the driving factors of electricity consumption changes in China, mainly focusing on the spatial spillover effects of electricity consumption which have been ignored by previous literature. Results show that economic growth and industry structure change have increased China’s electricity consumption by 8919 and 746 billion kWh, respectively, while the electricity efficiency improvement has reduced China’s electricity consumption by 5337 billion kWh for the entire period. Among the total decrease in China’s electricity consumption caused by electricity efficiency improvement, about 20% is caused by spatial spillover effects, which cannot be ignored. Moreover, there are great differences in electricity consumption changes’ components across China’s provinces. The results provide a quantitative and better understanding of the determinants of China’s electricity consumption changes, and practical implications for differentiated electricity consumption regulation policies and regional energy cooperation strategies for China, as well as for other similar countries.

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28

Poulin, Alain. "Characterization and modelling of electricity consumption." European Journal of Electrical Engineering 13, no. 5-6 (December 30, 2010): 717–40. http://dx.doi.org/10.3166/ejee.13.717-740.

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29

Nguyen, V. H. M., K. T. P. Nguyen, C. V. Vo, and B. T. T. Phan. "Forecast on 2030 Vietnam Electricity Consumption." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 2018): 2869–74. http://dx.doi.org/10.48084/etasr.2037.

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The first but very significant step in electricity system planning is to make an accurate long-term forecast on electricity consumption. This article aims to forecast the consumption for the Vietnam electricity system (GWH) up to 2030. An econometric model with the Cobb Douglas production function is used. The five variables proposed in the forecasting function are GDP, income, population, proportion of industry and service in GDP, and number of households. The forecasting equation is tested in terms of stationary and co-integration to choose meaningful variables and to eliminate the minor ones which account for none or small impacts on the forecast. The results show that: (1) the qualified forecasting equation only includes 3 major variables: the per capita income, the population, and the number of households, (2) with the medium income scenario, the forecasting consumptions in 2020, 2025, 2030 are 230,195 GWH, 349,949 GWH, 511,268 GWH respectively. (3) The GDP and the proportion of industry and service in GDP do not make major impacts on this forecasting in Vietnam. The method and the result of this article are likely a typical example of forecasting electricity consumption in developing countries which have a transforming economy similar to that in Vietnam.

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Ozoh, Patrick, Shapiee Abd-Rahman, and Jane Labadin. "A Predictive Framework for Electricity Consumption." Journal of IT in Asia 6, no. 1 (December 21, 2016): 25–35. http://dx.doi.org/10.33736/jita.331.2016.

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This study investigates the performance of regression model, Kalman filter adaptation algorithm and artificial neural network to assess their qualities for predictions. It develops predictive algorithms based on price, temperature and humidity as multiple variables affecting time-varying aspect of electricity consumption. In order to meet energy demand through the use of electricity as an energy source for daily activities in buildings such as air conditioning, lighting, computers and cooking stoves., adequate allocation of energy resources and planning should be done, including predicting for electricity consumption. The process involves collecting data from the power grid of Faculty of Computer Science and Information Technology building, Universiti Malaysia Sarawak. The forecasting techniques were tested on the data collected, and the dataset consists of electricity consumption readings, with electricity price, humidity and temperature included in the forecasting model. The performances of regression model, artificial neural network and Kalman algorithm were tested using statistical evaluation parameters, root mean squared error (RMSE) and mean absolute percentage error (MAPE); while the parameter, standard deviation, was used to check the validity of models. This study identified Kalman algorithm as the most effective method of predicting consumption data compared to regression model, and artificial neural network.

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Lambert, Sofie, Ward Van Heddeghem, Willem Vereecken, Bart Lannoo, Didier Colle, and Mario Pickavet. "Worldwide electricity consumption of communication networks." Optics Express 20, no. 26 (December 4, 2012): B513. http://dx.doi.org/10.1364/oe.20.00b513.

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Trujillo Romero, Felipe, Jose del Carmen Jimenez Hernandez, and Williams Gomez Lopez. "Predicting Electricity Consumption Using Neural Networks." IEEE Latin America Transactions 9, no. 7 (December 2011): 1066–72. http://dx.doi.org/10.1109/tla.2011.6129704.

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Terehovics, Edvins, Raimonda Soloha, Ivars Veidenbergs, and Dagnija Blumberga. "Analysis of fish refrigeration electricity consumption." Energy Procedia 147 (August 2018): 649–53. http://dx.doi.org/10.1016/j.egypro.2018.07.084.

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34

Liu, Jinshuo, Huiying Lan, Yizhen Fu, Hui Wu, and Peng Li. "Analyzing electricity consumption via data mining." Wuhan University Journal of Natural Sciences 17, no. 2 (March 17, 2012): 121–25. http://dx.doi.org/10.1007/s11859-012-0815-6.

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35

Woo, Chi-Keung. "Optimal electricity rates and consumption externality." Resources and Energy 10, no. 4 (December 1988): 277–92. http://dx.doi.org/10.1016/0165-0572(88)90007-2.

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36

Zhang, Chen, Hua Liao, and Zhifu Mi. "Climate impacts: temperature and electricity consumption." Natural Hazards 99, no. 3 (July 8, 2019): 1259–75. http://dx.doi.org/10.1007/s11069-019-03653-w.

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37

A. Al Metrik, Maissa, and Dhiaa A. Musleh. "Machine Learning Empowered Electricity Consumption Prediction." Computers, Materials & Continua 72, no. 1 (2022): 1427–44. http://dx.doi.org/10.32604/cmc.2022.025722.

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38

Gheorghe, A. C., E. Stan, and I. Udroiu. "Electricity Consumption Measurement System Using ESP32." Scientific Bulletin of Electrical Engineering Faculty 21, no. 2 (December 1, 2021): 23–26. http://dx.doi.org/10.2478/sbeef-2021-0017.

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Abstract The paper proposes the development of an IoT measurement system that offers the user the possibility to monitor and record the electricity consumed by a consumer through the Blynk application. In monitoring electricity one of its most important aspects is the calibration of the sensors used. Accurate and reliable detection is the key factor in measuring and managing electrical equipment. Fundamental physical quantities such as voltage and current are useful for determining all other electrical quantities. The ESP32 development board used to develop the system has 18 channels ADC (analog to digital converter) and supports a Wi-Fi connection at the access point and implicitly to the internet. The developed system can measure the voltage (U), current (I), power consumption (W) and consumption / hour (kWh) of a consumer, in my case for the consumer I used a light bulb with a power given by the manufacturer of 10W. The measured values are displayed on the LCD display of the system or through the Blynk application which also allows the registration of these values for a period of 12 months.

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39

Kim, Heesang, Hyeonseung Im, and Yang-Sae Moon. "Electricity Consumption Prediction using Generative Models." Transactions of The Korean Institute of Electrical Engineers 71, no. 1 (January 31, 2022): 218–24. http://dx.doi.org/10.5370/kiee.2022.71.1.218.

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40

Kim, Min-Jeong. "Determining the Relationship between Residential Electricity Consumption and Factors: Case of Seoul." Sustainability 12, no. 20 (October 16, 2020): 8590. http://dx.doi.org/10.3390/su12208590.

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Анотація:

This paper aims to determine the relationship between residential electricity consumption and other factors by analyzing the correlation and multiple regression between residential electricity consumption and three variables which are known as the factors affecting residential electricity consumption. We used the electricity consumption, income, number of household members, and age of 25 autonomous districts in Seoul as data for analysis, assuming that the socio-demographic characteristics vary from district to district in Seoul. The results showed that the electricity consumption and the three variables each had a significant correlation. However, multiple regression analysis results showed that the income and the number of household members have an effect on electricity consumption, but the average age is not a factor influencing electricity consumption. The results of this study would be useful for understanding the characteristics of urban residential electricity consumption in situations where the needs for an increase in residential electricity rates are continuously coming out.

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Morvai, Peter, Miroslav Žitňák, and Stanislav Paulovič. "Rationalization of Electricity Consumption in Househ Olds." Acta Technologica Agriculturae 21, no. 2 (June 1, 2018): 69–74. http://dx.doi.org/10.2478/ata-2018-0013.

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Abstract With rationalization of electricity consumption, it is possible to obtain savings of electric energy in households, as well as financing and capital input for the electricity production. Rational use of appliances in the best operating modes can reduce the final consumption of electricity, representing a positive impact on improving the environment quality. The main objective of this paper was to measure the electricity consumption of appliances in various operating modes. The measured values from two energy suppliers were recorded and processed in tables and figures, from which we created a table of the financial costs necessary for operation of appliances in different modes. For the calculation of annual electricity consumption and electricity prices, an application allowing selection of individual products from suppliers with current electricity prices was designed. According to the tables of electricity prices, various modes of appliance operation allow the selection of the most preferred mode for appliance operation based on the lowest price, rational consumption and energy costs. The aim of the paper is to demonstrate the consumption and costs of operating appliances in certain operating modes, standby modes and their efficient use or functionally similar appliances for reduction of electricity consumption in households.

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42

Lv, Tao, Duyang Pi, Xu Deng, Xiaoran Hou, Jie Xu, and Liya Wang. "Spatiotemporal Evolution and Influencing Factors of Electricity Consumption in the Yangtze River Delta Region." Energies 15, no. 5 (February 26, 2022): 1753. http://dx.doi.org/10.3390/en15051753.

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Electricity consumption accounts for a considerable part of the final energy consumption, and it is important for economic development and human life. This study explores the spatiotemporal evolution characteristics and influencing factors of electricity consumption in the Yangtze River Delta region in China from 2006 to 2019, using the gravity model and Logarithmic Mean Divisia Index method, respectively. The results show that: (1) The centers of gravity for the total final, industrial and residential electricity consumptions have a trend of migration towards the west. (2) The distance of migration of the center of gravity for residential electricity consumption is the highest, and the trend of migration of the center of gravity for industrial and total final electricity consumptions are synchronous. (3) Economic development is the main reason for the growth in regional electricity consumption, and the decrease in the investment electricity consumption intensity inhibits the growth of electricity consumption. This study provides references to restrain the excessive increase in electricity consumption and improve the layout of power facilities at the regional level.

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43

Duan, Ruijun, and Peng Guo. "Electricity Consumption in China: The Effects of Financial Development and Trade Openness." Sustainability 13, no. 18 (September 13, 2021): 10206. http://dx.doi.org/10.3390/su131810206.

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As China is facing the double pressure of economic growth as well as energy-saving and reduction of emissions, reducing electricity consumption without affecting economic development is a challenging and critical issue. Based on 31 provincial panel’s data in China from 2004 to 2018, this study empirically analyzes the direction and degree of the impact of financial development and trade openness on electricity consumption using the spatial econometric approach and panel vector autoregression (PVAR) model. The results indicate that China’s electricity consumption presents a significant spatial spill over effect, and the spatial agglomeration of electricity consumption in local regions is mainly HH clusters. A 1% positive change in financial development causes an increase of 0.089% in electricity consumption, but a 1% rise in financial development reduces electricity consumption of neighboring regions by 0.051%. A 1% positive change in trade openness decreases electricity consumption by 0.051%, while the spatial spillover effect of trade openness is not significant. It is also found that financial development has a long-term promoting effect on electricity consumption, while trade openness has a long-term inhibiting effect on electricity consumption.

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44

Ansu-Mensah, Peter, and Paul Adjei Kwakwa. "Modelling electricity consumption in Ghana: the role of financial development indicators." Green Finance 4, no. 1 (2021): 54–70. http://dx.doi.org/10.3934/gf.2022003.

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<abstract> <p>Access to electricity is touted as one of the ways of reducing poverty and improving the livelihoods of people. However, an increased consumption may also contribute to higher carbon dioxide emissions. While many studies have therefore assessed the determinants of electricity consumption for developing countries that have a lower electricity consumption and inadequate supply to meet demand, the effect of financial development on electricity consumption has been mixed. Consequently, this study models electricity consumption in Ghana with special attention on the effect of financial development. The results show that price reduces electricity consumption while income and population density increase consumption of electricity. When financial development is represented by domestic credit to private sector, domestic credit to private sector by banks and broad money supply, the effect is negative on electricity consumption. However, the effect is positive when financial development is represented by foreign direct investment. A financial index constructed from the four indicators shows financial development reduces electricity consumption in Ghana. Among other things the policy implication includes the need to formulate appropriate policy based on a specific indicator for financial development.</p> </abstract>

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45

Motlagh, Omid, Phillip Paevere, Tang Sai Hong, and George Grozev. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation." Applied Mathematics and Computation 270 (November 2015): 165–78. http://dx.doi.org/10.1016/j.amc.2015.08.029.

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46

Jamil, Faisal, and Eatzaz Ahmad. "The relationship between electricity consumption, electricity prices and GDP in Pakistan." Energy Policy 38, no. 10 (October 2010): 6016–25. http://dx.doi.org/10.1016/j.enpol.2010.05.057.

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47

Gao, Xumiao, Mingquan Wu, Ju Gao, Li Han, Zheng Niu, and Fang Chen. "Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images." Applied Sciences 12, no. 8 (April 14, 2022): 3971. http://dx.doi.org/10.3390/app12083971.

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The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

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48

Costa, Dora L., and Matthew E. Kahn. "Electricity Consumption and Durable Housing: Understanding Cohort Effects." American Economic Review 101, no. 3 (May 1, 2011): 88–92. http://dx.doi.org/10.1257/aer.101.3.88.

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We find that households living in California homes built in the 1960s and 1970s had high electricity consumption in 2000 relative to houses of more recent vintages because the price of electricity at the time of home construction was low. Homes built in the early 1990s had lower electricity consumption than homes of earlier vintages because the price of electricity was higher. The elasticity of the price of electricity at the time of construction was -0.22. As homes built between 1960 and 1989 become a smaller share of the housing stock, average household electricity purchases will fall.

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49

Tay, K. G., Y. Y. Choy, and C. C. Chew. "Forecasting Electricity Consumption Using Fuzzy Time Series." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 342. http://dx.doi.org/10.14419/ijet.v7i4.30.22305.

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Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. FTS is able to forecast UTHM electricity consumption quite well.

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50

Tajeuna, Etienne Gael, Mohamed Bouguessa, and Shengrui Wang. "Mining Customers’ Changeable Electricity Consumption for Effective Load Forecasting." ACM Transactions on Intelligent Systems and Technology 12, no. 4 (August 2021): 1–26. http://dx.doi.org/10.1145/3466684.

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Most existing approaches for electricity load forecasting perform the task based on overall electricity consumption. However, using such a global methodology can affect load forecasting accuracy, as it does not consider the possibility that customers’ consumption behavior may change at any time. Predicting customers’ electricity consumption in the presence of unstable behaviors poses challenges to existing models. In this article, we propose a principled approach capable of handling customers’ changeable electricity consumption. We devise a network-based method that first builds and tracks clusters of customer consumption patterns over time. Then, on the evolving clusters, we develop a framework that exploits long short-term memory recurrent neural network and survival analysis techniques to forecast electricity consumption. Our experiments on real electricity consumption datasets illustrate the suitability of the proposed approach.

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