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Journal articles on the topic 'Energy consumption'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

Boggs, Danny J. "Energy Consumption." Science 248, no. 4959 (June 1990): 1066. http://dx.doi.org/10.1126/science.248.4959.1066.b.

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2

Bodansky, David. "Energy Consumption." Science 242, no. 4877 (October 21, 1988): 348. http://dx.doi.org/10.1126/science.242.4877.348.a.

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3

Boggs, D. J. "Energy Consumption." Science 248, no. 4959 (June 1, 1990): 1066. http://dx.doi.org/10.1126/science.248.4959.1066-a.

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4

Sun, Zhong, Yingxia Yun, Na Li, and Zhonghua Xu. "Energy Consumption Building Design in Tianjin, China." Journal of Clean Energy Technologies 5, no. 5 (September 2017): 394–99. http://dx.doi.org/10.18178/jocet.2017.5.5.404.

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5

Berni, Mauro D., Ivo L. Dorileo, and Paulo C. Manduca. "Energy Consumption of Sugarcane and Corn Culture." Journal of Clean Energy Technologies 5, no. 5 (September 2017): 400–404. http://dx.doi.org/10.18178/jocet.2017.5.5.405.

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6

Matsumoto, Shigeru, Kenichi Mizobuchi, and Shunsuke Managi. "Household energy consumption." Environmental Economics and Policy Studies 24, no. 1 (November 29, 2021): 1–5. http://dx.doi.org/10.1007/s10018-021-00331-9.

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7

Ramos, Carlos, Zita Vale, Peter Palensky, and Hiroaki Nishi. "Sustainable Energy Consumption." Energies 14, no. 20 (October 14, 2021): 6665. http://dx.doi.org/10.3390/en14206665.

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8

Pablo-Romero, María P., Antonio Sánchez-Braza, and Manuel González-Pablo Romero. "Renewable energy in Latin America." AIMS Energy 10, no. 4 (2022): 695–717. http://dx.doi.org/10.3934/energy.2022033.

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<abstract> <p>Since the signing of the Paris Agreement in 2015, signatory countries have been adopting commitments to promote the use of renewable energy. Among the signatory countries, those of Latin America have stood out for the high percentage of renewables in their energy mix and their commitment to continue advancing towards energy decarbonization. This commitment implies the need to adequately recognize the starting point of renewable energy consumption in the region, and its relationship with the population and regional production. This study analyzes the evolution of renewable energy consumption in the Latin American region and its member countries, in relation to the Worldwide position, from 1993 to 2018. For this, the direct consumption of renewable energies and the energy used to generate electricity and heat, have been considered. These values are analyzed in Worldwide per capita and per unit production terms. The results show that the Latin American region has a higher percentage of renewables in its energy mix than Worldwide, with this percentage being even higher when considering only the consumption of renewable energies of indirect origin. Brazil stands out for the share of its renewable consumption. In terms of per capita renewable energy consumption, Latin America presents higher values than those achieved Worldwide, with a growing trend throughout the studied period. The renewable energy intensity is also higher in Latin America, with a decreasing trend, as experienced Worldwide.</p> </abstract>
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9

Lim, Seul-Ye, Jae-Hyung Park, and Seung-Hoon Yoo. "Assessment of the Economic Benefits from Electricity Consumption." Journal of Energy Engineering 24, no. 2 (June 30, 2015): 9–16. http://dx.doi.org/10.5855/energy.2015.24.2.009.

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10

Wiame, Ech-Chelfi. "Modelling Energy Consumption of Freight Road with MLR." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 71–79. http://dx.doi.org/10.5373/jardcs/v12sp1/20201048.

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11

Dr. Premakumara, G.S, Dr Premakumara, G. S. "Patterns and Determinants of Energy consumption in Karnataka." International Journal of Scientific Research 2, no. 7 (June 1, 2012): 110–12. http://dx.doi.org/10.15373/22778179/july2013/38.

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12

Bublyk, Yevhen, Olena Borzenko, and Anna Hlazova. "Cryptocurrency energy consumption: Analysis, global trends and interaction." Environmental Economics 14, no. 2 (August 18, 2023): 49–59. http://dx.doi.org/10.21511/ee.14(2).2023.04.

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The rapid spread of cryptocurrencies is one of the most relevant trends today. One of the significant risks of their spread is the increase in energy consumption, which has a negative impact on the environment due to carbon emissions. This requires the development of a scientific toolkit for assessing relationships and predicting the impact of cryptocurrencies on energy consumption, which is the aim of this paper.With the correlational regression analysis, the model of the dependence of spending on IT sector, energy consumption of Bitcoin, Ethereum and global capitalization of the cryptocurrency market was conducted, based on statistical data from Statista.com, Сoinmarketcap.com and International Data Corporation. To check the possible relationship, tests for the adequacy of the results obtained (Fisher’s test, Student’s t-test) confirmed the correctness of coefficients for independent variables.The results showed a significant direct correlation (Multiple R is 95%) of spending on IT sector, energy consumption and global capitalization of the cryptocurrency market. The established relationships allowed predicting that Bitcoin energy consumption may reach 142 Terawatt hours per year in 2026. And its impact on environment by mining in 2022 was at least 27.4 Mt of CO2 emission.As a proposal, a conclusion was made on the expediency of linking mining to the use of certain sources of electricity production, such as “residual” natural gas, nuclear power, renewable energy sources. The obtained results and conclusions may be used as a basis for political decisions in the field of energy efficiency and climate change mitigation.
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13

Mukarati, Julius, Leward Jeke, and Abel Sanderson. "Green technology and energy consumption efficiency in Zimbabwe." Environmental Economics 14, no. 1 (June 1, 2023): 73–80. http://dx.doi.org/10.21511/ee.14(1).2023.07.

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Environmental pollution is one of the major problems that has become an increasing area of concern globally, leading to the emergence of green energy technology. Research has been conducted on green technology adoption mainly in developed countries. However, there is noticeably limited knowledge about technology adoption and energy consumption in developing countries, for example, Zimbabwe. Thus, this paper seeks to analyze the impact of green technology adoption on energy sector performance in Zimbabwe. The results established that green technology adoption, energy pricing, energy sector investment, and capital structure significantly influence energy consumption efficiency. These results showed a positive relationship between green technology adoption and energy consumption efficiency based on the argument of the substitution possibility effect between green technology and energy demand. The study recommends adopting and identifying the type of green technology to utilize and the timing of investment in green technology. In addition, alternative estimation methods can be adopted to test the robustness of the findings.
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14

Kumar Rajeev Ranjan, Kameshwar. "Balancing Energy Consumption and Sustainability: An Indian Perspective." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 1853–61. http://dx.doi.org/10.21275/sr23723140704.

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15

Wik, Mette, Taoyuan Wei, Kristine Korneliussen, Rui Zhang, Solveig Glomsrød, and Qinghua Shi. "Drivers behind energy consumption by rural households in Shanxi." AIMS Energy 3, no. 4 (2015): 576–91. http://dx.doi.org/10.3934/energy.2015.4.576.

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16

Tchanche, Bertrand. "Energy consumption analysis of the transportation sector of Senegal." AIMS Energy 5, no. 6 (2017): 912–29. http://dx.doi.org/10.3934/energy.2017.6.912.

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17

Zhao, Liang, Ruobing Liang, Jili Zhang, Liangdong Ma, and Tianyi Zhao. "A new method for building energy consumption statistics evaluation: ratio of real energy consumption expense to energy consumption." Energy Systems 5, no. 4 (December 25, 2013): 627–42. http://dx.doi.org/10.1007/s12667-013-0111-3.

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18

Matiullah Ahsan, Zainab Zainal, Md Nor Ramdon Baharom, Ihsan Ullah Khalil, Azrul Mohd Ariffin, Nor Akmal Mohd Jamil, Irshad Ullah, Luqman Hakim Mehmod, and Mohd Fairouz Mohd Yousaf. "Consumption-Based Priority Algorithm for Energy Consumption." Journal of Advanced Research in Applied Sciences and Engineering Technology 38, no. 1 (January 24, 2024): 89–96. http://dx.doi.org/10.37934/araset.38.1.8996.

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Energy management is a critical component of intelligent, low-energy buildings. Demand-side management may benefit more from energy management. It could allow the system to use less energy while still meeting its demand for the available resources. According to recent research, buildings may reduce their energy use by up to 30% via improved operations, which are often based on effective energy management using the load priority algorithm. As a result, there is still room for energy savings in buildings via improved and effective operation. Smart grids provide electricity systems with suitable infrastructure for enhancing building energy efficiency. The secret to creating energy-efficient processes is creating rules based on energy consumption priority algorithms. When an energy bank is limited and an energy supply to a system is needed for a specific time, the priority algorithm is frequently used. In this study, a priority algorithm is put forward and put to the test in a real-world setting of a low-energy building.
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19

Yoo, Seung-Hoon, and Seung-Ryul Lee. "The Economic Value of Residential Electricity Consumption in Seoul." Journal of Energy Engineering 21, no. 1 (March 31, 2012): 81–85. http://dx.doi.org/10.5855/energy.2012.21.1.081.

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20

Lim, Kyoung-Min, Seul-Ye Lim, and Seung-Hoon Yoo. "Oil consumption and economic growth: A panel data analysis." Journal of Energy Engineering 23, no. 3 (September 30, 2014): 66–71. http://dx.doi.org/10.5855/energy.2014.23.3.066.

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21

Han, Joon. "LMDI Decomposition Analysis for Electricity Consumption in Korean Manufacturing." Journal of Energy Engineering 24, no. 1 (March 31, 2015): 137–48. http://dx.doi.org/10.5855/energy.2015.24.1.137.

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22

Abu, Rahaman, John Amakor, Rasaq Kazeem, Temilola Olugasa, Olusegun Ajide, Nosa Idusuyi, Tien-Chien Jen, and Esther Akinlabi. "Modeling influence of weather variables on energy consumption in an agricultural research institute in Ibadan, Nigeria." AIMS Energy 12, no. 1 (2024): 256–70. http://dx.doi.org/10.3934/energy.2024012.

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<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>
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23

Lairgi, Lamya, Rachid Lagtayi, Yassir Lairgi, Abdelmajd Daya, Rabie Elotmani, Ahmed Khouya, and Mohammed Touzani. "Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method." AIMS Energy 11, no. 5 (2023): 795–809. http://dx.doi.org/10.3934/energy.2023039.

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<abstract> <p>Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m<sup>2</sup>. While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.</p> </abstract>
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24

Reinhard Neiber, Josef, and Stefan Neser. "Energy Consumption and Improvement of the Energy Efficiency in the Agricultural Animal Husbandry." Modern Environmental Science and Engineering 1, no. 5 (November 2015): 207–17. http://dx.doi.org/10.15341/mese(2333-2581)/05.01.2015/001.

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25

Moezzi, Mithra. "Decoupling Energy Efficiency from Energy Consumption." Energy & Environment 11, no. 5 (September 2000): 521–37. http://dx.doi.org/10.1260/0958305001500301.

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26

Tseng, Sheng-Wen, and Yen-Yu Chen. "How Did the Changes in Industrial and Energy Structure Influence Energy Consumption in Shandong, China?" Journal of Clean Energy Technologies 9, no. 1 (January 2021): 1–11. http://dx.doi.org/10.18178/jocet.2021.9.1.524.

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The Gross Regional Product (GRP) of Shandong ranks third in China, but its energy and coal consumption rank first. However, in the past studies, no effort was made to analyze the influence of Shandong energy conservation and emission reduction policies on energy consumption changes. To make up for this gap, the revised divisia index and the energy consumption structure methods were used in this study to analyze the driving force of changes in energy consumption in Shandong from 2005 to 2016. The results of this research show that: Firstly, the control of energy-intensive industries and strong energy conservation policies had become the main driving forces for energy density reduction. Secondly, the energy structure optimization policies only increased the proportion of hydro, nuclear and new energy production to replace a proportion of oil, but could not effectively reduce the consumption and proportion of coal. The continuous increase in coal consumption offset the energy conservation effect by key industries during the Twelfth Five-Year Plan period. It is clear that a reduction in the amount of coal used and an increase in the proportion of hydro, nuclear and new energy (especially in the industrial sector) is at the core of the energy problems in Shandong. Policy recommendations are proposed that are based on the findings of this study.
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27

Lu, Hong Wei, and Hong Wei Gong. "An Analysis of the Seasonal Energy Consumption of Culture and Education Comprehensive Building." Advanced Materials Research 724-725 (August 2013): 874–79. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.874.

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Through a comparative analysis, this paper attempts to summarize the hourly variations of the total energy consumptions and itemized energy consumptions in a culture and education comprehensive building in summer, winter, and the transitional season. The results show a great difference in energy consumptions in the same building in different seasons. Energy consumption is highest in winter. There is a minor difference in energy consumptions between a typical day in summer vacation and a normal work day in summer. The lowest energy consumption is in winter vacation, but a typical day in winter vacation would still consume about half of the energy consumed on a normal working day in winter. Keywords: Comprehensive building; analysis of energy consumption; itemized energy consumption
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28

Gholami, Roya, Rohit Nishant, and Ali Emrouznejad. "Modeling Residential Energy Consumption." Journal of Global Information Management 29, no. 2 (March 2021): 166–93. http://dx.doi.org/10.4018/jgim.2021030109.

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Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.
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29

Lorch, J. R., and A. J. Smith. "Apple Macintosh's energy consumption." IEEE Micro 18, no. 6 (1998): 54–63. http://dx.doi.org/10.1109/40.743684.

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30

KASAHARA, Makoto. "Survey of Energy Consumption." Input-Output Analysis 16, no. 2 (2008): 16–26. http://dx.doi.org/10.11107/papaios.16.16.

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31

Fronza, Ilenia, Nabil El Ioini, Luis Corral, Matthias Moroder, and Moritz Moroder. "Monitoring multicopters energy consumption." ACM SIGITE Newsletter 13, no. 1 (April 23, 2018): 11. http://dx.doi.org/10.1145/3210251.3210254.

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32

Tamimi, A., and Z. Kodah. "Energy consumption in Jordan." Energy 17, no. 11 (November 1992): 1013–17. http://dx.doi.org/10.1016/0360-5442(92)90018-u.

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33

Sözen, Adnan, Erol Arcaklioğlu, and Mehmet Özkaymak. "Turkey’s net energy consumption." Applied Energy 81, no. 2 (June 2005): 209–21. http://dx.doi.org/10.1016/j.apenergy.2004.07.001.

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34

Davis, Mark. "Rural household energy consumption." Energy Policy 26, no. 3 (February 1998): 207–17. http://dx.doi.org/10.1016/s0301-4215(97)00100-6.

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35

DESAI, ASHOK V. "DEVELOPMENT AND ENERGY CONSUMPTION." Oxford Bulletin of Economics and Statistics 40, no. 3 (May 1, 2009): 263–72. http://dx.doi.org/10.1111/j.1468-0084.1978.mp40003005.x.

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36

Berg, Sanford V., and Prakash Loungani. "Modelling state energy consumption." Energy Economics 12, no. 3 (July 1990): 216–26. http://dx.doi.org/10.1016/0140-9883(90)90034-d.

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37

Beeston, Rob. "Sound, Technics, Energy: (Consumption)." Sociological Review 49, no. 2_suppl (October 2001): 73–96. http://dx.doi.org/10.1111/j.1467-954x.2001.tb03550.x.

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38

COLLISON, ROGER. "Energy consumption during cooking." International Journal of Food Science & Technology 14, no. 2 (June 28, 2007): 173–79. http://dx.doi.org/10.1111/j.1365-2621.1979.tb00861.x.

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39

Clark, Brett, Andrew K. Jorgenson, and Jeffrey Kentor. "Militarization and Energy Consumption." International Journal of Sociology 40, no. 2 (July 2010): 23–43. http://dx.doi.org/10.2753/ijs0020-7659400202.

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40

Roberts, Andrew, Caroline Stewart, Gaynor Cole, Sybil Farmer, and John Patrick. "‘Energy consumption in spasticity’." Developmental Medicine & Child Neurology 44, no. 4 (February 13, 2007): 284–86. http://dx.doi.org/10.1111/j.1469-8749.2002.tb00808.x.

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41

Degefa, Mehari Weldemariam. "Ethiopian energy consumption forecast." Mehran University Research Journal of Engineering and Technology 41, no. 4 (October 1, 2022): 42. http://dx.doi.org/10.22581/muet1982.2204.04.

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This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.
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42

Glaser, J. A. "US renewable energy consumption." Clean Technologies and Environmental Policy 9, no. 4 (October 6, 2007): 249–52. http://dx.doi.org/10.1007/s10098-007-0117-4.

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43

Roberts, Andrew, Caroline Stewart, Gaynor Cole, Sybil Farmer, and John Patrick. "Energy consumption in spasticity." Developmental Medicine and Child Neurology 44, no. 04 (May 9, 2002): 283. http://dx.doi.org/10.1017/s0012162201222075.

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44

G, Rupa. "Caffeinated Energy Drink Consumption among First Year Medical Students." Journal of Medical Science And clinical Research 05, no. 03 (March 3, 2017): 18437–43. http://dx.doi.org/10.18535/jmscr/v5i3.27.

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45

M.S, Mr Muneshwara, Dr Anil G.N, and Dr Thungamani M. "Reducing Energy Consumption in Smart System through Mobilouds Framework." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 627–35. http://dx.doi.org/10.31142/ijtsrd6998.

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46

Chao Wang, Chao Wang, Wen-Wen Zha Chao Wang, Cheng Zhu Wen-Wen Zha, Wen-Yang Wang Cheng Zhu, Guang-Bo Li Wen-Yang Wang, Liang Tao Guang-Bo Li, Hui-Min Ma Liang Tao, and Jun Jiao Hui-Min Ma. "LEACH Clustering Routing Protocol Based on Balanced Energy Consumption." 電腦學刊 33, no. 5 (October 2022): 151–62. http://dx.doi.org/10.53106/199115992022103305013.

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<p>Faced with the problems of unbalanced energy consumption (EC) and short lifetime of nodes in Wireless Sensor Networks (WSN), a Low Energy Adaptive Clustering Hierarchy (LEACH) clustering routing protocol based on energy balance, namely LEACH-EB (LEACH Based on Energy Balance) protocol was proposed. At the initial selection stage, the nodes which are close to the base station (BS) with great remaining energy and many neighbor nodes are selected as the cluster heads (CHs); then, the non-CH nodes enter the clusters which have the least costs based on the strength and remaining energy of the communication signals between themselves and different CHs. At the data transmission stage, if the CH which sends the information is one hop away from BS, the CH needs to select a neighbor CH with the largest forwarding probability as the next hop relay node based on the remaining energy of each neighbor CH, the number of nodes in the cluster, and the distance from BS. The selected neighbor CH continues to determine the next hop in the above manner until the data is successfully sent to BS. Simulation tests show that LEACH-EB protocol can receive more data and extend the network life cycle by 60%, 43.1%, and 13.36% compared with LEACH, LEACH-C, and FIGWO, respectively. </p> <p>&nbsp;</p>
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47

Mistry, Vrushank. "Use of Artificial Intelligence in Optimizing HVAC Energy Consumption." International Journal of Science and Research (IJSR) 10, no. 4 (April 5, 2021): 1372–78. http://dx.doi.org/10.21275/sr24203193927.

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48

CERMIKLI, A., and Harun OZTRKLER. "THE WORLD ENERGY CONSUMPTION: CHANGING in ENERGY CONSUMPTION in 1980-2005 PERIOD." Ekonomik Yaklasim 21, no. 74 (2010): 1. http://dx.doi.org/10.5455/ey.10698.

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49

Sarada, M. "Optimizing Thermal Energy Consumption in Cement Process Plants: Strategies for Energy Conservation and Sustainability." International Journal of Science and Research (IJSR) 12, no. 8 (August 5, 2023): 2044–46. http://dx.doi.org/10.21275/sr23822210455.

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50

Lim, Ki Choo. "Development of Bottom-up model for Residential Energy Consumption by Use." Journal of Energy Engineering 22, no. 1 (March 31, 2013): 38–43. http://dx.doi.org/10.5855/energy.2013.22.1.038.

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