Volume 8, Issue 2, June 2019, Page: 87-97
Modeling and Prediction of New Energy Use
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
Received: Apr. 21, 2019;       Published: Jun. 15, 2019
DOI: 10.11648/j.jenr.20190802.16      View  160      Downloads  32
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.
Keywords
Multiple Regression Analysis, Multi-attribute Decision Making, Principal Component Analysis
To cite this article
Ruiming Yang, Leiyuan Li, Modeling and Prediction of New Energy Use, Journal of Energy and Natural Resources. Vol. 8, No. 2, 2019, pp. 87-97. doi: 10.11648/j.jenr.20190802.16
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