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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (1): 56-62.doi: 10.6040/j.issn.1672-3961.0.2019.180

• 电气工程 • 上一篇    下一篇

能源消费发展及预测方法综述

杨明1,2(),杜萍静1,刘凤全1,郝旭鹏1,孛一凡1   

  1. 1. 电网智能化调度与控制教育部重点实验室(山东大学)，山东 济南 250061
    2. 山东大学全球能源联网战略技术研究院，山东 济南 250061
  • 收稿日期:2019-04-22 出版日期:2020-02-20 发布日期:2020-02-14
  • 作者简介:杨明(1980-),男,山东烟台人,教授,博导,主要研究方向为电力系统控制优化与运行.E-mail:myang@sdu.edu.en,杨明，1980年8月出生，工学博士，教授，博士生导师，山东大学电气工程学院副院长，全球能源互联网战略技术研究院副院长；中国电力教育协会电气工程学科教学委员会副主任委员、IEEE高级会员。IEEE Transactions on Industry Applications、IET Renewable Power Generation、Protection and Control of Modern Power Systems副编辑，IEEE IAS I & CPS Asia Funding Committee副主席，IEC SC8A “可再生能源接入电网”分技术委员会委员
  • 基金资助:
    全球能源互联网集团有限公司资助项目(GEIGC-S-[2018]068)

Review of energy consumption and demand forecasting methods

Ming YANG1,2(),Pingjing DU1,Fengquan LIU1,Xupeng HAO1,Yifan BO1   

  1. 1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China
    2. Global Energy Interconnection Strategy and Technology Research Institute, Shandong University, Jinan 250061, Shandong, China
  • Received:2019-04-22 Online:2020-02-20 Published:2020-02-14
  • Supported by:
    全球能源互联网集团有限公司资助项目(GEIGC-S-[2018]068)

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摘要：

针对能源规划、发展对能源需求预测依赖度的增加和能源需求预测难度上升的问题，对各种能源预测方法与能源发展方向进行了探讨。从近年来全球能源需求发展的方向入手分析当前能源发展格局对能源预测方法的需求现状；对现有的主要能源预测方法进行归纳、对比，总结现有研究方法的利弊和适用场合；结合能源发展的新方向，对未来的能源预测发展方向进行探讨与展望，并应用LEAP模型对非洲地区进行能源需求预测分析，分析区域互补效应以及“电能替代”对能源需求发展的作用。

关键词: 能源需求, 能源需求预测, 预测模型, 全球能源互联网, 电能替代

Abstract:

In view of the increasing dependence of energy planning on energy demand forecasting and the difficulty of energy demand forecasting, this paper analyzed various energy forecasting methods and discussed the direction of energy development. The article analyzed the current demand situation of energy development methods from the direction of global energy demand development in recent years. The existing main energy forecasting methods were summarized and compared. The advantages and disadvantages of the existing research methods and applicable occasions were summarized. Combined with the new direction of energy development, the future development prospects of energy forecasting were given. Furthermore, this paper applied the LEAP model to predict the energy demand of the African region, and analyzed the regional energy complementation effect and the role of "electricity substitution" in the development of energy demand.

Key words: energy demand, energy demand forecast, prediction model, global energy internet, electricity substitution

中图分类号: 

  • TM60

图1

全球一次能源消费变化图"

表1

主要电能替代形式"

电能替代形式 影响领域 电能替代途径 被替代对象
以电代煤 工业 蓄热电锅炉电炊具、电采暖设备 燃煤锅炉
居民生活 暖设备 煤气灶
工业 电水泵 油泵
以电代油 交通业 电动汽车、电气铁路、港口岸电 燃油汽车、燃油发电机
农业 电气排灌 燃油电动机
以电代气 居民生活 电炊具、电热装置 燃气灶、燃气锅炉

图2

2017—2040年非洲电力需求量及其占比情况"

1 舒印彪, 薛禹胜, 蔡斌, 等. 关于能源转型分析的评述(一)转型要素及研究范式[J]. 电力系统自动化, 2018, 42 (9): 1- 15.
SHU Yinbiao , XUE Yusheng , CAI Bin , et al. A review of energy transition part one elements and paradigms[J]. Power System Automation, 2018, 42 (9): 1- 15.
2 张恒旭, 施啸寒, 刘玉田, 等. 我国西北地区可再生能源基地对全球能源互联网构建的支撑作用[J]. 山东大学学报(工学版), 2016, 46 (4): 96- 102.
ZHANG Hengxu , SHI Xiaohan , LIU Yutian , et al. The supporting role of renewable energy bases in Northwest China on the construction of global energy Internet[J]. Journal of Shandong University (Engineering Science), 2016, 46 (4): 96- 102.
3 焦冰琦, 张富强, 徐志成. 基于时序场景的全球联网电力流多期规划模型[J]. 全球能源互联网, 2019, 2 (1): 8- 15.
JIAO Bingqi , ZHAMG Fuqiang , XU Zhicheng . A multi-period planning model for global networked power flow based on time series[J]. Global Energy Interconnection, 2019, 2 (1): 8- 15.
4 石访, 张恒旭, 张磊. 全球能源互联网宏观运行特性仿真框架[J]. 山东大学学报(工学版), 2017, 47 (6): 151- 156.
SHI Fang , ZHANG Hengxu , ZHANG Lei . Simulation framework for macro-operating characteristics of GEI[J]. Journal of Shandong University (Engineering Science), 2017, 47 (6): 151- 156.
5 杨经纬, 张宁, 王毅, 等. 面向可再生能源消纳的多能源系统:述评与展望[J]. 电力系统自动化, 2018, 42 (4): 11- 24.
YANG Jingwei , ZHANG Ning , WANG Yi , et al. Multi-energy system for renewable energy consumption: review and prospect[J]. Power System Automation, 2018, 42 (4): 11- 24.
6 刘振亚. 全球能源互联网[M]. 北京: 中国电力出版社, 2015: 57- 64.
LIU Zhenya . Global energy interconnection[M]. Beijing: China Electric Power Press, 2015: 57- 64.
7 张英杰.我国能源需求预测及其结构优化研究[D].北京:华北电力大学, 2016.
ZHANG Yingjie.China's energy demand forecasting and its structural optimization research[D].Beijing: North China Electric Power University, 2016.
8 BP.Statistical review of world energy 2018[R].London: [s.n.], 2018.
9 邓鸿鹄.北京市能源消费预测方法比较研究[D].北京:北京林业大学, 2013.
DENG Hongjun.Comparative study on energy consumption forecasting methods in Beijing[D].Beijing: Beijing Forestry University, 2013.
10 邹一舟.基于遗传灰色神经网络的中国能源消费量预测研究[D].武汉:华中科技大学, 2017.
ZOU Yizhou.Research on China's energy consumption forecast based on genetic grey neural network[D].Wuhan: Huazhong University of Science and Technology, 2017.
11 何悦.中国能源供需预测模型及电能替代对策研究[D].北京:北京交通大学, 2018.
HE Yue.China's energy supply and demand forecasting model and research on electric energy replacement countermeasures[D].Beijing: Beijing Jiaotong University, 2018.
12 LIANG Qiaomei , FAN Yan , WEI Yiming . Multi-regional input-output model for regional energy requirements and CO2 emissions in China[J]. Energy Policy, 2007, 35 (3): 1685- 1700.
doi: 10.1016/j.enpol.2006.04.018
13 王家诚, 阎修桐. 用部门分析法预测能源需求量[J]. 中国能源, 1983, (1): 9- 10.
WANG Jiacheng , YAN Xiutong . Prediction of energy demand by sector analysis[J]. Energy Sources in China, 1983, (1): 9- 10.
14 李笋, 王超, 张桂林, 等. 基于支持向量回归的短期负荷预测[J]. 山东大学学报(工学版), 2017, 47 (6): 52- 56.
LI Sun , WANG Chao , ZHANG Guilin , et al. Short-term power load forecasting based on support vector regression[J]. Journal of Shandong University (Engineering Science), 2017, 47 (6): 52- 56.
15 汪行, 范中启. 基于改进GM(1, 1)模型的能源消费预测研究[J]. 煤炭技术, 2017, 36 (8): 305- 307.
doi: 10.13301/j.cnki.ct.2017.08.122
WANG Xing , FAN Zhongqi . Research on energy consumption prediction based on improved GM(1, 1) model[J]. Coal Technology, 2017, 36 (8): 305- 307.
doi: 10.13301/j.cnki.ct.2017.08.122
16 王健, 魏立力, 全晓静. 基于ARMA模型的宁夏能源消费预测[J]. 赤峰学院学报(自然科学版), 2015, 31 (3): 3- 6.
doi: 10.3969/j.issn.1673-260X.2015.03.002
WANG Jian , WEI Lili , QUAN Xiaojing . Energy consumption forecast of Ningxia based on ARMA model[J]. Journal of Chifeng College (Natural Science), 2015, 31 (3): 3- 6.
doi: 10.3969/j.issn.1673-260X.2015.03.002
17 ZENG Li , FU Jingying , GUANG Lin , et al. Multi-scenario analysis of energy consumption and carbon emissions: the case of Hebei province in China[J]. Energies, 2019, 12 (4): 624.
doi: 10.3390/en12040624
18 王海涛, 宁云才. 基于改进GM(1, 1)模型的新疆煤炭消费预测[J]. 数学的实践与认识, 2018, 48 (14): 192- 196.
WANG Haitao , NING Yuncai . Prediction of Xinjiang coal consumption based on improved GM(1, 1) model[J]. Mathematics in Practice and Theory, 2018, 48 (14): 192- 196.
19 XU Ning , DANG Yaoguo , GONG Yande . Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China[J]. Energy, 2017, 118, 473- 480.
doi: 10.1016/j.energy.2016.10.003
20 李灿.基于改进BP神经网络的负荷预测问题研究[D].西安:西安理工大学, 2018.
LI Can.Research on load forecasting problem based on improved BP neural network[D]. Xian: Xi'an University of Technology, 2018.
21 YU Feng , XU Xiaodong . A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network[J]. Applied Energy, 2014, 134, 102- 113.
doi: 10.1016/j.apenergy.2014.07.104
22 燕景. 基于BP神经网络数字的省级能源预测:河南的视角[J]. 数字通信世界, 2018, (11): 144.
doi: 10.3969/J.ISSN.1672-7274.2018.11.121
YAN Jing . Provincial-level energy prediction based on BP neural network digital Henan perspective[J]. Digital Communication World, 2018, (11): 144.
doi: 10.3969/J.ISSN.1672-7274.2018.11.121
23 KIALASHAKI Arash.Evaluation and forecast of energy consumption in different sectors of the United States using artificial neural networks[D].Wisconsin: The University of Wisconsin-Milwaukee, 2014.
24 魏云云. 组合的灰色关联度和GA-BP模型对能源需求的预测分析[J]. 兰州文理学院学报(自然科学版), 2018, 32 (5): 27- 30.
WEI Yunyun . Predictive analysis of energy demand by combined grey correlation degree and GA-BP model[J]. Journal of Lanzhou University of Arts and Science (Natural Science Edition), 2018, 32 (5): 27- 30.
25 卢奇, 顾培亮, 邱世明. 组合预测模型在我国能源消费系统中的建构及应用[J]. 系统工程理论与实践, 2003, (3): 24- 30.
doi: 10.3321/j.issn:1000-6788.2003.03.005
LU Qi , GU Peiliang , QIU Shiming . Construction and application of combined forecasting model in China's energy consumption system[J]. Systems Engineering-Theory & Practice, 2003, (3): 24- 30.
doi: 10.3321/j.issn:1000-6788.2003.03.005
26 文炳洲, 索瑞霞. 基于组合模型的我国能源需求预测[J]. 数学的实践与认识, 2016, 46 (20): 45- 53.
WEN Bingzhou , SUO Ruixia . Forecast of China's energy demand based on combination model[J]. Mathematics Practice and Theory, 2016, 46 (20): 45- 53.
27 LIANG Qiaomei , FAN Ying , WEI Yiming . Multi-regional input-output model for regional energy requirements and CO2 emissions in China[J]. Energy Policy, 2007, 35 (3): 1685- 1700.
doi: 10.1016/j.enpol.2006.04.018
28 CHI Yuanying , YUAN Lina , LI Hongying , et al. Using LEAP model to predict energy consumption of Beijing under the constraint of low-carbon economy[J]. Ekoloji, 2019, 28 (107): 1205- 1211.
29 EMODI Nnaemeka , EMODI Chinenye , MURTHY Girish , et al. Energy policy for low carbon development in Nigeria: a LEAP model application[J]. Renewable and Sustainable Energy Reviews, 2017, 68, 247- 261.
doi: 10.1016/j.rser.2016.09.118
30 HUANG Yophy , BOR Yunchang , PENG Chiyu . The long-term forecast of Taiwan's energy supply and demand: LEAP model application[J]. Energy Policy, 2011, 39 (11): 6790- 6803.
doi: 10.1016/j.enpol.2010.10.023
31 陈睿, 饶政华, 刘继雄, 等. 基于LEAP模型的长沙市能源需求预测及对策研究[J]. 资源科学, 2017, 39 (3): 482- 489.
CHEN Rui , RAO Zhenghua , LIU Jixiong , et al. The energy demand forecast and countermeasure research of Changsha city based on LEAP model[J]. Resources Science, 2017, 39 (3): 482- 489.
32 HU Guangxiao , MA Xiaoming , JI Junping . Scenarios and policies for sustainable urban energy development based on LEAP model:a case study of a postindustrial city:Shenzhen China[J]. Applied Energy, 2019, 238, 876- 886.
doi: 10.1016/j.apenergy.2019.01.162
33 MIRJAT Hussain , UQAILI A , HARIJAN K , et al. Long-term electricity demand forecast and supply side scenarios for Pakistan (2015—2050):a LEAP model application for policy analysis[J]. Energy, 2018, 165, 512- 526.
34 刘振亚. 全球能源互联网跨国跨洲互联研究及展望[J]. 中国电机工程学报, 2016, 36 (19): 5103- 5110.
LIU Zhenya . Research of global clean energy resource and power grid interconnection[J]. Proceedings of the CSEE, 2016, 36 (19): 5103- 5110.
35 李文华.首届"中俄能源投资论坛"在莫斯科成功举办[N].中国能源报, 2014-10-27(004).
LI Wenhua.The first Sino-Russian energy investment forum was successfully held in Moscow[N].China Energy News, 2014-10-27(004).
36 张玉红, 张彦涛, 张栋, 等. 东北亚地区跨国电力联网模式及技术可行性初步研究[J]. 全球能源互联网, 2018, 1 (S1): 213- 221.
ZHANG Yuhong , ZHANG Yantao , ZHANG Dong , et al. A preliminary study on the model and technical feasibility of transnational power network in Northeast Asia[J]. Global Energy Interconnetion, 2018, 1 (S1): 213- 221.
37 孙毅, 周爽, 单葆国, 等. 多情景下的电能替代潜力分析[J]. 电网技术, 2017, 41 (1): 118- 123.
SUN Yi , ZHOU Shuang , SHAN Baoguo , et al. Analysis of electric energy substitution potential under multiple scenarios[J]. Power System Technology, 2017, 41 (1): 118- 123.
38 赵秋莉, 冯君淑, 金艳鸣, 等. 全球能源互联网背景下的环境效益评估[J]. 全球能源互联网, 2018, 1 (增刊1): 257- 262.
ZHAO Qiuli , FENG Junshu , JIN Yanming , et al. Environmental benefit basessment under the background of global energy internet[J]. Global Energy Interconnetion, 2018, 1 (Suppl.1): 257- 262.
39 孙毅, 许鹏, 单葆国, 等. 售电侧改革背景下"互联网+"电能替代发展路线[J]. 电网技术, 2016, 40 (12): 3648- 3654.
doi: 10.13335/j.1000-3673.pst.2016.12.004
SUN Yi , XU Peng , SHAN Baoguo , et al. The alternative development route of "Internet+" energy under the background of sales side reform[J]. Power System Technology, 2016, 40 (12): 3648- 3654.
doi: 10.13335/j.1000-3673.pst.2016.12.004
40 WIRYADINATA Steven , MODERA Mark , JENKINS Bryan , et al. Technical and economic feasibility of unitary, horizontal ground-loop geothermal heat pumps for space conditioning in selected California climate zones[J]. Energy & Building, 2016, 42, 127- 132.
41 埃勒哈穆易卜拉欣. 全球能源互联网助力非洲经济转型与发展[J]. 中国电力企业管理, 2017, (22): 20- 21.
ELHAMU Ibrahim . Global energy internet helps Africa's economic transformation and development[J]. China Electric Power Enterprise Management, 2017, (22): 20- 21.
42 张士宁, 杨方, 陆宇航, 等. 全球能源互联网发展指数研究[J]. 全球能源互联网, 2018, 1 (5): 537- 548.
ZHANG Shining , YANG Fang , LU Yuhang , et al. Research on global energy internet development index[J]. Global Energy Interconnection, 2018, 1 (5): 537- 548.
43 李隽, 宋福龙, 余潇潇. 全球能源互联网骨干网架规划研究[J]. 全球能源互联网, 2018, 1 (5): 527- 536.
LI Jun , SONG Fulong , YU Xiaoxiao . Research on global energy internet backbone network planning[J]. Global Energy Internet, 2018, 1 (5): 527- 536.
44 INTERNATIONAL Energy Agency.World enery outlook 2018[R]. Paris: [s.n.], 2018.
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