考虑复杂影响因素的上海市负荷预测研究
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摘要
电力负荷预测是电力系统规划的基础性工作之一,是实现自动发电控制和经济调度控制的前提。针对电力系统复杂、众多的影响因素,文章基于城市用电构成与城市用地划分,提出了一种综合考虑了经济、工业、城市化、城市居民生活等影响因素的负荷预测指标体系,并采用聚类分析法和BP神经网络进行负荷预测,为快速准确估算上海市各区域负荷增长提供了有效途径,为上海电力系统规划提供了可靠支撑。
Power load forecasting is one of the basic tasks of power system planning, and it is the premise of automatic generation control and economic dispatching control. In view of the complexity and many influencing factors of power system, based on the composition of urban power consumption and the division of urban land, this paper puts forward a load forecasting index system which comprehensively considers the influencing factors such as economy, industry, urbanization, urban residents' life and so on.The cluster analysis method and BP neural network are used for load forecasting, which provides an effective way to quickly and accurately estimate the regional load growth in Shanghai, and provides a reliable support for the power system planning in Shanghai.
Power load forecasting is one of the basic tasks of power system planning, and it is the premise of automatic generation control and economic dispatching control. In view of the complexity and many influencing factors of power system, based on the composition of urban power consumption and the division of urban land, this paper puts forward a load forecasting index system which comprehensively considers the influencing factors such as economy, industry, urbanization, urban residents' life and so on.The cluster analysis method and BP neural network are used for load forecasting, which provides an effective way to quickly and accurately estimate the regional load growth in Shanghai, and provides a reliable support for the power system planning in Shanghai.
引文
[1]康重庆,夏清,刘梅.电力系统负荷预测.第2版[M].北京:中国电力出版社,2017.
[2]黄湘君.基于主成分分析的BP神经网络在电力系统负荷预测中的应用[J].电力与能源,2008(16):313-314.
[3]何晓萍,刘希颖.中国城市化进程中的电力需求预测[J].经济研究,2009(1):118-130.
[4]宋雅慧.上海电力需求分析———基于1990-2013年数据[D].上海:上海师范大学,2015.
[5]李亦言,等.复杂城市化因素下的饱和负荷预测模型[J].电网技术,2016(09):2824-2831.
[6]刘思.配电网空间负荷聚类及预测方法研究[D].杭州:浙江大学,2017.
[7]程倩.上海城市化特性影响因素下最大电力负荷预测的研究[A].以供给侧结构性改革引领能源转型与创新———第十三届长三角能源论坛论文集[C].杭州,2016:6.
[8]沈勇,等.上海地区居民用电的影响因素和需求预测模型[J].华东电力,2012,40(10):1763-1766.
[9]黄晨宏.影响上海电力需求的主要因素及电力需求预测[J].电力需求侧管理,2011,13(03):72-76.
[2]黄湘君.基于主成分分析的BP神经网络在电力系统负荷预测中的应用[J].电力与能源,2008(16):313-314.
[3]何晓萍,刘希颖.中国城市化进程中的电力需求预测[J].经济研究,2009(1):118-130.
[4]宋雅慧.上海电力需求分析———基于1990-2013年数据[D].上海:上海师范大学,2015.
[5]李亦言,等.复杂城市化因素下的饱和负荷预测模型[J].电网技术,2016(09):2824-2831.
[6]刘思.配电网空间负荷聚类及预测方法研究[D].杭州:浙江大学,2017.
[7]程倩.上海城市化特性影响因素下最大电力负荷预测的研究[A].以供给侧结构性改革引领能源转型与创新———第十三届长三角能源论坛论文集[C].杭州,2016:6.
[8]沈勇,等.上海地区居民用电的影响因素和需求预测模型[J].华东电力,2012,40(10):1763-1766.
[9]黄晨宏.影响上海电力需求的主要因素及电力需求预测[J].电力需求侧管理,2011,13(03):72-76.