Modeling and Prediction of New Energy Use

Ruiming Yang, Leiyuan Li
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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.
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新能源利用建模与预测
本文以美国加利福尼亚州(CA)、亚利桑那州(AZ)、新墨西哥州(NM)和德克萨斯州(TX)四个州的清洁能源消费为研究对象,通过对能源消费方式、能源构成的异同及其产生的原因进行分析比较,找出具有最优能源消费方式的州,并在此基础上对这些州未来的能源构成进行预测,提出州际能源协定的目标。并通过多元回归分析,以及各州能源消费方式的相应指标,对各州的新能源消费方式进行比较,并从行业和地域的角度分析各州的差异,为后续的建模准备必要的参考。在对数据进行基础分析后,通过能源构成、清洁能源消费量等5个指标,建立了寻找能源构成最优状态的多属性决策模型;在分析的基础上,我们发现了这些州的能源消费的不同特征。然后根据近20年的能源消费数据和四州2025年和2050年的能源消费预测,建立GM(1,1)模型进行预测。采用多属性决策的方法,找出能源构成最优的状态,并以该状态下2年的清洁能源消费为基础,提出能源契约目标。在分析了这四个州能源消费方式的差异后,为了协调和整合这四个州的能源生产和消费,我们提出了6条行动建议。除了多元回归分析之外,多属性决策对于这四种状态下的能耗分析,主成分分析也起着重要的作用。该方法有助于发现不同能源消耗方式的意义,计算出这四种状态下当前和未来的能源消耗,以及最优的能源消耗方法。最后,通过对不同模型的比较,我们得出了几乎相同的结论:CA是一种能量组合最优的状态,对未来的发展具有最佳实践意义。在预测2025年和2050年的能源消耗时,我们可以将CA作为参考状态,并为这四个状态之间的能源约定设定目标。
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