Modeling and Prediction of New Energy Use

Ruiming Yang; Leiyuan Li

doi:doi:10.11648/j.jenr.20190802.16

| Peer-Reviewed

Modeling and Prediction of New Energy Use

Ruiming Yang, Leiyuan Li

Published in Journal of Energy and Natural Resources (Volume 8, Issue 2)

Received: 21 April 2019 Accepted: Published: 15 June 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper looks into clean energy consumption in the four states of California (CA), Arizona (AZ), New Mexico (NM) and Texas (TX) by analyzing and comparing the methods of energy consumption, the similarity and difference of their energy composition and the causes for it, and finding out the state with the optimal ways of energy consumption, and based on it, predicts the future energy composition of these states and proposes a target for interstate energy convention. And through multiple regression analysis, and the corresponding indicators of the methods of energy consumption in these states, we compare the ways of new energy consumption in these states, and analyze the difference from the perspective of industries and geographies in these states, which prepares necessary reference for the following modeling. After some basic analysis of the data, we establish a multi-attribute decision making to find a state with optimal composition of energies through the five indicators of energy composition, volume of clean energy consumption etc; and based on the analysis, we find the different characteristics of energy consumption in these states. Then we set up a GM (1, 1) model to make prediction based on the data of energy consumption of the near 20 years and project energy consumption of the four states in 2025 and 2050. By means of multi-attribute decision making, we find out the state with optimal energy composition, and propose a target of the energy convention based on a two-year clean energy consumption in this state. After analyzing the difference of energy consumption methods in these four states, and in order to coordinate and integrate energy production and consumption in these states, we propose the 6 suggestions for action. In addition to the multiple regression analysis, multi-attribute decision making for the analysis of the energy consumption in these four states, principal component analysis also plays an important role. This method helps to find the significance of different ways of energy consumption, figure out the current and future energy consumption in these four states, and the state with optimal energy consumption method. Finally, by means of comparing with different models, we have nearly the same conclusion: CA is a state with optimal energy combination and has best practice for future development. There in projecting the 2025 and 2050 energy consumption, we can use CA as a reference state and set such as the target for energy convention between these four states.

Published in	Journal of Energy and Natural Resources (Volume 8, Issue 2)
DOI	10.11648/j.jenr.20190802.16
Page(s)	87-97
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Multiple Regression Analysis, Multi-attribute Decision Making, Principal Component Analysis

References

[1]	Y. Sue, S. Y. To, and M. Goto, “The intermediate approach to sustainability enhancement and scale related measures in environmental assessment,” EUROPEAN JOURNAL OF OPERATIONALRESEARCH, American, vol. 276, 2019, pp. 744-756.
[2]	Y. Gao, Y. Chen, L. Chen, “Experimental investigation on the permeability of a hydrate-bearing reservoir considering over burden pressure”. FUEL. American, vol. 246, 2019, pp. 308-318.
[3]	C. Ekici, I. Teke, “Global solar radiation estimation from measurements of visibility and air temperature extremes,”. ENERGY SOURCES PART ARECOVER YUTI LIZATION AND ENVIRON MENTAL EFFECTS, American, vol. 41, 2019, pp. 1344-1359.
[4]	T. P. Miyanawala, R. K. Jaiman, “Decomposition of wake dynamics in fluid-structure interaction via low-dimensional models,” JOURNAL OF FLUIDMECHANICS, American, vol. 867, 2019, pp. 723-764.
[5]	L. M. Aguiar, J. Polo, J. M. Vindel, “Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty,” RENEWABLEENERGY, American, vol. 135, MAY 2019, pp. 98-107.
[6]	X. Xu, C. Chen, “Energy efficiency and energy justice for US low-income households: Ananalysis of multifaceted challenges and potential” ENERGYPOLICY, American, vol. 128, pp. 763-774, MAY 2019.
[7]	N. Habibi, I. Howard, S. Simani, “Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: Areview,” RENEWABLE ENERGY. American. vol. 135, pp. 877-896, MAY 2019.
[8]	X. Han, T. Sun, Q. Feng, “Study on environmental pollution loss measurement model of energy consumption emits and its application in industrial parks,” SCIENCE OF THETOTALENVIRONMENT, American, vol. 668, 2019, pp. 1259-1266.
[9]	Yuezheng, Gong; Jia, Zhao, “Energy-stable Runge-Kutta schemes for gradient flow models using the energy quadratization approach”, APPLIED MATHEMATICS LETTERS vol. 94, 2019, pp. 224-231.
[10]	Yu Yan; Qi Li; Weirong Chen, “Optimal Energy Management and Control in Multimode Equivalent Energy Consumption of Fuel Cell/Super capacitor of Hybrid Electric Tram” vol: 66, 2019, pp. 6065-6076.
[11]	Xie Yulong; Mendon Vrushali; Halverson, Mark; Assessing overall building energy “performance of a large population of residential single-family homes using limited field data” Vol. 12, 2019, pp. 480-493.

Cite This Article

Plain Text BibTeX RIS

APA Style

Ruiming Yang, Leiyuan Li. (2019). Modeling and Prediction of New Energy Use. Journal of Energy and Natural Resources, 8(2), 87-97. https://doi.org/10.11648/j.jenr.20190802.16

Copy | Download

ACS Style

Ruiming Yang; Leiyuan Li. Modeling and Prediction of New Energy Use. J. Energy Nat. Resour. 2019, 8(2), 87-97. doi: 10.11648/j.jenr.20190802.16

Copy | Download

AMA Style

Ruiming Yang, Leiyuan Li. Modeling and Prediction of New Energy Use. J Energy Nat Resour. 2019;8(2):87-97. doi: 10.11648/j.jenr.20190802.16

Copy | Download

@article{10.11648/j.jenr.20190802.16,
  author = {Ruiming Yang and Leiyuan Li},
  title = {Modeling and Prediction of New Energy Use},
  journal = {Journal of Energy and Natural Resources},
  volume = {8},
  number = {2},
  pages = {87-97},
  doi = {10.11648/j.jenr.20190802.16},
  url = {https://doi.org/10.11648/j.jenr.20190802.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20190802.16},
  abstract = {This paper looks into clean energy consumption in the four states of California (CA), Arizona (AZ), New Mexico (NM) and Texas (TX) by analyzing and comparing the methods of energy consumption, the similarity and difference of their energy composition and the causes for it, and finding out the state with the optimal ways of energy consumption, and based on it, predicts the future energy composition of these states and proposes a target for interstate energy convention. And through multiple regression analysis, and the corresponding indicators of the methods of energy consumption in these states, we compare the ways of new energy consumption in these states, and analyze the difference from the perspective of industries and geographies in these states, which prepares necessary reference for the following modeling. After some basic analysis of the data, we establish a multi-attribute decision making to find a state with optimal composition of energies through the five indicators of energy composition, volume of clean energy consumption etc; and based on the analysis, we find the different characteristics of energy consumption in these states. Then we set up a GM (1, 1) model to make prediction based on the data of energy consumption of the near 20 years and project energy consumption of the four states in 2025 and 2050. By means of multi-attribute decision making, we find out the state with optimal energy composition, and propose a target of the energy convention based on a two-year clean energy consumption in this state. After analyzing the difference of energy consumption methods in these four states, and in order to coordinate and integrate energy production and consumption in these states, we propose the 6 suggestions for action. In addition to the multiple regression analysis, multi-attribute decision making for the analysis of the energy consumption in these four states, principal component analysis also plays an important role. This method helps to find the significance of different ways of energy consumption, figure out the current and future energy consumption in these four states, and the state with optimal energy consumption method. Finally, by means of comparing with different models, we have nearly the same conclusion: CA is a state with optimal energy combination and has best practice for future development. There in projecting the 2025 and 2050 energy consumption, we can use CA as a reference state and set such as the target for energy convention between these four states.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Prediction of New Energy Use
AU  - Ruiming Yang
AU  - Leiyuan Li
Y1  - 2019/06/15
PY  - 2019
N1  - https://doi.org/10.11648/j.jenr.20190802.16
DO  - 10.11648/j.jenr.20190802.16
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 87
EP  - 97
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20190802.16
AB  - This paper looks into clean energy consumption in the four states of California (CA), Arizona (AZ), New Mexico (NM) and Texas (TX) by analyzing and comparing the methods of energy consumption, the similarity and difference of their energy composition and the causes for it, and finding out the state with the optimal ways of energy consumption, and based on it, predicts the future energy composition of these states and proposes a target for interstate energy convention. And through multiple regression analysis, and the corresponding indicators of the methods of energy consumption in these states, we compare the ways of new energy consumption in these states, and analyze the difference from the perspective of industries and geographies in these states, which prepares necessary reference for the following modeling. After some basic analysis of the data, we establish a multi-attribute decision making to find a state with optimal composition of energies through the five indicators of energy composition, volume of clean energy consumption etc; and based on the analysis, we find the different characteristics of energy consumption in these states. Then we set up a GM (1, 1) model to make prediction based on the data of energy consumption of the near 20 years and project energy consumption of the four states in 2025 and 2050. By means of multi-attribute decision making, we find out the state with optimal energy composition, and propose a target of the energy convention based on a two-year clean energy consumption in this state. After analyzing the difference of energy consumption methods in these four states, and in order to coordinate and integrate energy production and consumption in these states, we propose the 6 suggestions for action. In addition to the multiple regression analysis, multi-attribute decision making for the analysis of the energy consumption in these four states, principal component analysis also plays an important role. This method helps to find the significance of different ways of energy consumption, figure out the current and future energy consumption in these four states, and the state with optimal energy consumption method. Finally, by means of comparing with different models, we have nearly the same conclusion: CA is a state with optimal energy combination and has best practice for future development. There in projecting the 2025 and 2050 energy consumption, we can use CA as a reference state and set such as the target for energy convention between these four states.
VL  - 8
IS  - 2
ER  -

Copy | Download

Author Information

Ruiming Yang

Department of Communications and Information Engineering, Century College, Beijing University of Posts and Telecommunications, Beijigng, China
Leiyuan Li

Department of Communications and Information Engineering, Century College, Beijing University of Posts and Telecommunications, Beijigng, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Ruiming Yang, Leiyuan Li. (2019). Modeling and Prediction of New Energy Use. Journal of Energy and Natural Resources, 8(2), 87-97. https://doi.org/10.11648/j.jenr.20190802.16

Copy | Download

ACS Style

Ruiming Yang; Leiyuan Li. Modeling and Prediction of New Energy Use. J. Energy Nat. Resour. 2019, 8(2), 87-97. doi: 10.11648/j.jenr.20190802.16

Copy | Download

AMA Style

Ruiming Yang, Leiyuan Li. Modeling and Prediction of New Energy Use. J Energy Nat Resour. 2019;8(2):87-97. doi: 10.11648/j.jenr.20190802.16

Copy | Download

@article{10.11648/j.jenr.20190802.16,
  author = {Ruiming Yang and Leiyuan Li},
  title = {Modeling and Prediction of New Energy Use},
  journal = {Journal of Energy and Natural Resources},
  volume = {8},
  number = {2},
  pages = {87-97},
  doi = {10.11648/j.jenr.20190802.16},
  url = {https://doi.org/10.11648/j.jenr.20190802.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20190802.16},
  abstract = {This paper looks into clean energy consumption in the four states of California (CA), Arizona (AZ), New Mexico (NM) and Texas (TX) by analyzing and comparing the methods of energy consumption, the similarity and difference of their energy composition and the causes for it, and finding out the state with the optimal ways of energy consumption, and based on it, predicts the future energy composition of these states and proposes a target for interstate energy convention. And through multiple regression analysis, and the corresponding indicators of the methods of energy consumption in these states, we compare the ways of new energy consumption in these states, and analyze the difference from the perspective of industries and geographies in these states, which prepares necessary reference for the following modeling. After some basic analysis of the data, we establish a multi-attribute decision making to find a state with optimal composition of energies through the five indicators of energy composition, volume of clean energy consumption etc; and based on the analysis, we find the different characteristics of energy consumption in these states. Then we set up a GM (1, 1) model to make prediction based on the data of energy consumption of the near 20 years and project energy consumption of the four states in 2025 and 2050. By means of multi-attribute decision making, we find out the state with optimal energy composition, and propose a target of the energy convention based on a two-year clean energy consumption in this state. After analyzing the difference of energy consumption methods in these four states, and in order to coordinate and integrate energy production and consumption in these states, we propose the 6 suggestions for action. In addition to the multiple regression analysis, multi-attribute decision making for the analysis of the energy consumption in these four states, principal component analysis also plays an important role. This method helps to find the significance of different ways of energy consumption, figure out the current and future energy consumption in these four states, and the state with optimal energy consumption method. Finally, by means of comparing with different models, we have nearly the same conclusion: CA is a state with optimal energy combination and has best practice for future development. There in projecting the 2025 and 2050 energy consumption, we can use CA as a reference state and set such as the target for energy convention between these four states.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Prediction of New Energy Use
AU  - Ruiming Yang
AU  - Leiyuan Li
Y1  - 2019/06/15
PY  - 2019
N1  - https://doi.org/10.11648/j.jenr.20190802.16
DO  - 10.11648/j.jenr.20190802.16
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 87
EP  - 97
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20190802.16
AB  - This paper looks into clean energy consumption in the four states of California (CA), Arizona (AZ), New Mexico (NM) and Texas (TX) by analyzing and comparing the methods of energy consumption, the similarity and difference of their energy composition and the causes for it, and finding out the state with the optimal ways of energy consumption, and based on it, predicts the future energy composition of these states and proposes a target for interstate energy convention. And through multiple regression analysis, and the corresponding indicators of the methods of energy consumption in these states, we compare the ways of new energy consumption in these states, and analyze the difference from the perspective of industries and geographies in these states, which prepares necessary reference for the following modeling. After some basic analysis of the data, we establish a multi-attribute decision making to find a state with optimal composition of energies through the five indicators of energy composition, volume of clean energy consumption etc; and based on the analysis, we find the different characteristics of energy consumption in these states. Then we set up a GM (1, 1) model to make prediction based on the data of energy consumption of the near 20 years and project energy consumption of the four states in 2025 and 2050. By means of multi-attribute decision making, we find out the state with optimal energy composition, and propose a target of the energy convention based on a two-year clean energy consumption in this state. After analyzing the difference of energy consumption methods in these four states, and in order to coordinate and integrate energy production and consumption in these states, we propose the 6 suggestions for action. In addition to the multiple regression analysis, multi-attribute decision making for the analysis of the energy consumption in these four states, principal component analysis also plays an important role. This method helps to find the significance of different ways of energy consumption, figure out the current and future energy consumption in these four states, and the state with optimal energy consumption method. Finally, by means of comparing with different models, we have nearly the same conclusion: CA is a state with optimal energy combination and has best practice for future development. There in projecting the 2025 and 2050 energy consumption, we can use CA as a reference state and set such as the target for energy convention between these four states.
VL  - 8
IS  - 2
ER  -

Copy | Download