The Medium- and Long-Term Power Demand Forecasting for Jiangsu Province with Consideration of Different Developing Situations

doi:10.11930/j.issn.1004-9649.2016.07.072.05

Abstract

Abstract: Different weights of various indicators in different development stages are not considered in the conventional medium- and long-term combined forecasting model, and different development scenarios are not considered either in the medium- and long-term forecasting. Based on the periodicity of economy and its relationship with electric power, the electricity data are grouped according to different development stages, and two indicators including electricity error and trend error are used to calculate the weight of each single model. The entropy method is adopted to get the comprehensive weight of each single forecasting method. With a full consideration of different scenarios of future economic development and changes of electricity technology such as electric vehicle and demand response, a combined forecasting is made respectively for different development scenarios including low-speed development, high-speed development and other factors. The combined forecasting model has been applied to the medium- and long-term power load forecasting of Jiangsu Province and satisfactory results are obtained under different scenarios.

Key words: power demand forecasting, load forecasting, entropy method, scenario analysis, combined forecasting model

CLC Number:

TM715

LONG Yu, WANG Xiaoying, ZHOU Qi, JIANG Nan, LUO Xin. The Medium- and Long-Term Power Demand Forecasting for Jiangsu Province with Consideration of Different Developing Situations[J]. Electric Power, 2016, 49(7): 72-76.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.07.072.05

https://www.electricpower.com.cn/EN/Y2016/V49/I7/72

References

[1] 龙瑞华,毛弋,毛李帆,等. 基于诱导有序加权平均算子和马尔可夫链的中长期电力负荷组合预测模型[J]. 电网技术,2010, 34（3）：150-156.
LONG Ruihua, MAO Yi, MAO Lifan, et al . A combination model for medium- and long-term load forecasting based on induced ordered weighted averaging operator and markov chain[J]. Power System Technology, 2010, 34(3): 150-156.
[2] 郑雅楠,单葆国,顾宇桂,等. 中长期电量灰色预测中数据预处理方法研究[J]. 中国电力,2013,46（10）：111-114.
ZHENG Yanan, SHAN Baoguo, GU Yugui, et al . Data preprocessing for grey model of medium-long term load forecasting [J]. Electric Power, 2013, 46(10): 111-114.
[3] 张成,滕欢. 基于偏最小二乘法与BP神经网络的电力中长期负荷预测[J]. 电力建设,2012,33（7）：26-29.
ZHANG Cheng, TENG Huan. Mid-long term power load forecasting based on PLSR and BP neural network[J]. Electric Power Construction, 2012, 33(7): 26-29.
[4] 李瑾,刘金朋,王建军. 采用支持向量机和模拟退火算法的中长期负荷预测方法[J]. 中国电机工程学报,2011,31（16）：63-66.
LI Jin, LIU Jinpeng, WANG Jianjun. Mid-long term load forecasting based on simulated annealing and SVM algorithm[J]. Proceedings of the CSEE, 2011, 31(16): 63-66.
[5] 温青,张筱慧,杨旭. 基于负荷误差和经济发展趋势的组合预测模型在中长期负荷预测中的应用[J]. 电力系统保护与控制, 2011,39（3）：57-61.
WEN Qing, ZHANG Xiaohui, YANG Xu. The application of combination forecasting model in medium-long term load forecasting based on load error and economic development trend[J]. Power System Protection and Control, 2011, 39(3): 57-61.
[6] 余健明,燕飞,杨文宇,等. 中长期电力负荷的变权灰色组合预测模型[J]. 电网技术,2005,29（17）：26-29.
YU Jianming, YAN Fei, YANG Wenyu, et al . Gray variable weight combination model for middle and long term load forecasting [J]. Power System Technology, 2005, 29(17): 26-29.
[7] 周湶,任海军,李健,等. 层次结构下的中长期电力负荷变权组合预测方法[J]. 中国电机工程学报,2010,30（16）：47-52.
ZHOU Quan, REN Haijun, LI Jian, et al . Variable weight combination method for mid-long term power load forecasting based on hierarchical structure[J]. Proceedings of the CSEE, 2010, 30(16): 47-52.

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