A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting

能源消耗 消费(社会学) 可解释性 竞赛(生物学) 计算机科学 能量(信号处理) 鉴定(生物学) 计量经济学 乘法函数 运筹学 经济 数学 人工智能 工程类 统计 生态学 社会科学 社会学 数学分析 电气工程 生物 植物
作者
Yunxin Zhang,Huan Guo,Ming Sun,Sifeng Liu,Jeffrey Yi‐Lin Forrest
出处
期刊:Energy [Elsevier BV]
卷期号:264: 126154-126154 被引量:14
标识
DOI:10.1016/j.energy.2022.126154
摘要

Energy is the foundation for the stable operation and long-term growth of the national economy. Quantifying the degree of competition and cooperation among different types of energy consumption and predicting its future development trend will help to analyze the changes in energy consumption structure, to better formulate and make decisions on energy policies. In view of the inherent complexity of the energy consumption system structure, this paper proposes a novel grey Lotka–Volterra model (GLVM) for energy consumption forecasting, to evaluate the impact of long-term competition and cooperation on the national energy consumption system and its development trend. In theory, the solution method and parameter identification of the GLVM are given, and the parameter characteristics of GLVM under multiplicative transformation are analyzed. Based on this, the GLVM is used to analyze the consumption structure of different types of energy in China, the United States and Germany, and quantitatively analyzes the internal relationship of the energy consumption structure of the three representative countries. Compared with other models, the results show that this model is superior to other existing models in accuracy and interpretability. The model proposed in this paper is of great value to researchers and decision makers.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
zrt发布了新的文献求助10
刚刚
小雒雒完成签到,获得积分10
1秒前
极品小亮发布了新的文献求助10
1秒前
王丽雅发布了新的文献求助10
1秒前
2秒前
JamesPei应助江海小舟采纳,获得10
2秒前
2秒前
英姑应助淡然又菡采纳,获得10
3秒前
3秒前
66小鼠发布了新的文献求助10
3秒前
干净芹菜完成签到 ,获得积分10
4秒前
星辰大海应助yss采纳,获得10
4秒前
5秒前
5秒前
Moonpie完成签到 ,获得积分10
5秒前
端庄双双发布了新的文献求助10
5秒前
小马甲应助cangyingshi采纳,获得10
6秒前
6秒前
6秒前
6秒前
8秒前
FashionBoy应助Zzsfe163采纳,获得10
8秒前
orixero应助可可采纳,获得20
8秒前
8秒前
Ava应助卡咖滴采纳,获得10
8秒前
找不着发布了新的文献求助10
8秒前
Daisypharma完成签到,获得积分10
9秒前
9秒前
9秒前
橘柚完成签到,获得积分10
9秒前
11秒前
小马甲应助散装洋芋采纳,获得10
12秒前
12秒前
12秒前
13秒前
lt1014发布了新的文献求助10
13秒前
完美世界应助panpanpan采纳,获得10
13秒前
qiu完成签到,获得积分10
13秒前
13秒前
顾矜应助zz采纳,获得10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Picture this! Including first nations fiction picture books in school library collections 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Cowries - A Guide to the Gastropod Family Cypraeidae 1200
ON THE THEORY OF BIRATIONAL BLOWING-UP 666
Signals, Systems, and Signal Processing 610
“美军军官队伍建设研究”系列(全册) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6385858
求助须知:如何正确求助?哪些是违规求助? 8199582
关于积分的说明 17344275
捐赠科研通 5439410
什么是DOI,文献DOI怎么找? 2876690
邀请新用户注册赠送积分活动 1853100
关于科研通互助平台的介绍 1697270