基于数据挖掘技术的中长期负荷预测方法

Abstract
Figure/Table
References
Related Citation (6)

Download: PDF (769 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The power load prediction can guide and make decisions for the rational planning of power grid. This article uses the data mining technology for medium and long-term load forecasting in Nantong area. Firstly, cluster analysis is applied to classify all feeder data in Nantong, and the correlation analysis and grey correlation analysis are used to analyze the degree of influence of external factors on the load changes. Then these influential factors are used as the input of the neural network to establish a neural network model based on the clustering radial basis function (RBF), and the load forecasting results are obtained. Finally, compared with the prediction without considering clustering and only using RBF neural network model, the experimental results show that the method in this paper is more advanced, significantly improves the load prediction accuracy, and achieves the purpose of ensuring the reliability of power supply.

Key words： data mining technology cluster analysis correlation analysis association rules load classification

Received: 09 October 2020

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHANG Min
	QIAN Shuangqiu
	WU Zhongqi
	WANG Ziyuan

Cite this article:

ZHANG Min,QIAN Shuangqiu,WU Zhongqi等. Load forecasting method based on data mining technology[J]. Electrical Engineering, 2021, 22(6): 43-48.

URL:

http://dqjs.cesmedia.cn/EN/Y2021/V22/I6/43

[1] 王雁凌, 吴梦凯. 经济新常态下基于偏最小二乘回归的中长期负荷预测模型[J]. 电力自动化设备, 2018, 38(3): 133-139.
[2] LÓPEZ M, VALERO S, SANS C, et al. Comparison of short-term load forecasting performance by neural network and autoregressive based models[C]//2018 15th International Conference on the European Energy Market (EEM), 2018: 1-5.
[3] 马莉, 张凡, 杨素. 电力市场改革重大问题及解析[J]. 电力设备管理, 2020(6): 36-37.
[4] 傅靖, 王栋, 白阳, 等. 基于基因表达式编程的中长期电力负荷预测挖掘[J]. 电力信息与通信技术, 2020, 18(5): 7-12.
[5] 罗澍忻, 麻敏华, 蒋林, 等. 考虑多时间尺度数据的中长期负荷预测方法[J]. 中国电机工程学报, 2020, 40(增刊1): 11-19.
[6] 贺远, 翟丹丹, 苏贵敏. 基于PCA与改进BP神经网络相结合的电网中长期负荷预测[J]. 电力大数据, 2019, 22(5): 74-80.
[7] 张志, 杜延菱, 崔慧军, 等. 考虑关联因素的智能化中长期电力负荷预测方法[J]. 电力系统保护与控制, 2019, 47(2): 24-30.
[8] 熊宁, 肖异瑶, 姚志刚, 等. 基于农村发展模式分析的中长期负荷预测方法[J]. 电力系统及其自动化学报, 2021, 33(3): 94-101.
[9] 张籍, 薛儒涛, 刘慧, 等. 基于深度信念网络的不同行业中长期负荷预测[J]. 电力系统及其自动化学报, 2019, 31(9): 12-19, 27.
[10] 阮振, 吕林, 刘友波, 等. 采用深度学习和多维模糊C均值聚类的负荷分类方法[J]. 电力系统及其自动化学报, 2019, 31(7): 43-50.
[11] 吴云, 雷建文, 鲍丽山, 等. 基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测[J]. 电力系统自动化, 2018, 42(20): 67-72.
[12] 肖琪. 基于优化K-means算法的电力负荷分类研究[D]. 大连: 大连理工大学, 2015.
[13] 崔青, 方欣, 张志磊, 等. 基于模糊c均值算法和改进归一化的变压器故障诊断方法[J]. 电气技术, 2019, 20(11): 46-48, 67.
[14] 吴文心. 依托层次分析和模糊聚类方法的发达地域中长期负荷预测研究[J]. 电气技术, 2017, 18(10): 74-78, 84.
[15] 沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析[J]. 电工技术学报, 2017, 32(16): 265-274.
[16] 刘剑, 邰能灵, 范春菊, 等. 基于暂态电流Pearson相关性的两电平VSC-HVDC直流线路故障判别[J]. 电工技术学报, 2017, 32(3): 74-85.
[17] 杨楠, 李宏圣, 袁景颜, 等. 计及灰色关联度分析的中长期负荷灰色预测方法[J]. 电力系统及其自动化学报, 2018, 30(6): 108-114.
[18] ABDULSALAM Y G, MUSTAPHA L A, JAKAWA J N, et al.LSTM network for predicting medium to long term electricity usage in residential buildings (Rikkos Jos-City, Nigeria)[J]. Computer Systems Science and Engineering, 2019, 9(2): 21-30.
[19] 石德琳. 基于神经网络的电力负荷预测研究与实现[D]. 济南: 山东大学, 2016.
[20] 李晶. 基于数据挖掘的电力负荷特性与用户分类研究[D]. 北京: 北京邮电大学, 2016.