Short-term Parallel Power Load Forecasting Based on Balanced KNN

doi:10.11930/j.issn.1004-9649.201708078

Abstract

Abstract: To improve the accuracy of load forecasting and cope with the challenge of single computer's insufficient computing resource under massive and high-dimension data, a short-term load forecasting model based on balanced KNN algorithm is proposed. In order to improve the accuracy of scene division, the weight of load characteristics is quantified by using the anti-entropy weight method; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The BP neural network algorithm is used to train and predict the load; Adopting the Apache Spark programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 30-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm, besides, the proposed forecasting algorithm possesses excellent parallel performance.

Key words: load forecasting, load scenes, K-means, balanced KNN, BP neural network, Apache Spark

CLC Number:

TM715

LIN Fang, LIN Yan, LV Xianlong, CHENG Xingong, ZHANG Huiyu, CHEN Bojian. Short-term Parallel Power Load Forecasting Based on Balanced KNN[J]. Electric Power, 2018, 51(10): 88-94,102.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201708078

https://www.electricpower.com.cn/EN/Y2018/V51/I10/88

References

[1] 康重庆, 夏清, 张伯明. 电力系统负荷预测研究综述与发展方向的探讨[J]. 电力系统自动化, 2004, 28(17):1-11 KANG Chongqing, XIA Qing, ZHANG Boming. Review of power system load forecasting and its development[J]. Automation of Electric Power Systems, 2004, 28(17):1-11
[2] 廖旎焕, 胡智宏, 马莹莹, 等. 电力系统短期负荷预测方法综述[J]. 电力系统保护与控制, 2011, 39(1):147-152 LIAO Nihuan, HU Zhihong, MA Yingying, et al. Review of the short-term load forecasting methods of electric power system[J]. Power System Protection and Control, 2011, 39(1):147-152
[3] 唐俊杰, 牛焕娜, 杨明皓, 等. 基于线性相关分析的周期自回归短期负荷预测[J]. 电力系统保护与控制, 2010, 38(14):128-133 TANG Junjie, NIU Huanna, YANG Minghao, et al. Periodic autoregressive short-term forecasting method based on the linear correlation analysis[J]. Power System Protection and Control, 2010, 38(14):128-133
[4] 张振高, 杨正瓴. 短期负荷预测中的负荷求导法及天气因素的使用[J]. 电力系统及其自动化学报, 2006, 18(5):79-83 ZHANG Zhengao, YANG Zhengling. Load derivation in short term forecasting using weather factor[J]. Proceedings of the CSU-EPSA, 2006, 18(5):79-83
[5] 吴越强, 吴文传, 李飞, 等. 基于鲁棒Holt-Winter模型的超短期配变负荷预测方法[J]. 电网技术, 2014, 38(10):2810-2815 WU Yueqiang, WU Wenchuan, LI Fei, et al. Ultra-short term load forecasting using robust Holt-Winter in distribution network[J]. Power System Technology, 2014, 38(10):2810-2815
[6] 韩力, 韩学山, 贠志皓, 等. 多节点超短期负荷预测方法[J]. 电力系统自动化, 2007, 31(21):30-34 HAN Li, HAN Xueshan, YUN Zhihao, et al. Classification linkage optimization model of time of use power price between generating side and retail side[J]. Automation of Electric Power Systems, 2007, 31(21):30-34
[7] 张涛, 赵登福, 周琳, 等. 基于RBF神经网络和专家系统的短期负荷预测方法[J]. 西安交通大学学报, 2001, 35(4):331-334 ZHANG Tao, ZHAO Dengfu, ZHOU Lin, et al. Short-term load forecasting using radial basis function networks and expert system[J]. Journal of Xi'an Jiaotong University, 2001, 35(4):331-334
[8] 雷绍兰, 孙才新, 周湶, 等. 基于径向基神经网络和自适应神经模糊系统的电力短期负荷预测方法[J]. 中国电机工程学报, 2005, 25(22):78-82 LEI Shaolan, SUN Caixin, ZHOU Quan, et al. Short-term load forecasting method based on RBF neural network and ANFIS system[J]. Proceedings of the CSEE, 2005, 25(22):78-82
[9] 李霄, 王昕, 郑益慧, 等. 基于改进最小二乘支持向量机和预测误差校正的短期风电负荷预测[J]. 电力系统保护与控制, 2015, 43(11):63-69 LI Xiao, WANG Xin, ZHENG Yihui, et al. Short-term wind load forecasting based on improved LSSVM and error forecasting correction[J]. Power System Protection and Control, 2015, 43(11):63-69
[10] 曾鸣, 吕春泉, 田廓, 等. 基于细菌群落趋药性优化的最小二乘支持向量机短期负荷预测方法[J]. 中国电机工程学报, 2011, 31(34):93-99 ZENG Ming, LU Chunquan, TIAN Kuo, et al. Least squares-support vector machine load forecasting approach optimized by bacterial colony chemotaxis method[J]. Proceedings of the CSEE, 2011, 31(34):93-99
[11] 朱六章. 短期负荷预测的组合数据挖掘算法[J]. 电力系统自动化, 2006, 30(14):82-86 ZHU Liuzhang. Short-term eletric load forecasting with combined data mining algorithm[J]. Automation of Electric Power Systems, 2006, 30(14):82-86
[12] 孙宏斌, 郭庆来, 潘昭光, 等. 能源互联网:理念、架构与前沿展望[J]. 电力系统自动化, 2015, 39(19):1-8 SUN Hongbi, GUO Qinglai, PAN Zhaoguang, et al. Energy internet:concept, architecture and frontier outlook[J]. Automation of Electric Power Systems, 2015, 39(19):1-8
[13] 赵俊华, 文福拴, 薛禹胜, 等. 云计算:构建未来电力系统的核心计算平台[J]. 电力系统自动化, 2010, 34(15):1-8 ZHAN Junhua, WEN Fushuan, XUE Yusheng, et al. Cloud computing:implementing an essential computing platform for future power systems[J]. Automation of Electric Power Systems, 2010, 34(15):1-8
[14] 罗军舟, 金嘉晖, 宋爱波, 等. 云计算:体系架构与关键技术[J]. 通信学报, 2011, 32(7):3-21 LUO Junzhou, JIN Jiahui, SONG Aibo, et al. Cloud computing:architecture and key technologies[J]. Journal on Communications, 2011, 32(7):3-21
[15] 林闯, 苏文博, 孟坤, 等. 云计算安全:架构、机制与模型评价[J]. 计算机学报, 2013, 36(9):1765-1784 LIN Chuang, SU Wenbo, MENG Kun, et al. Cloud computing security:architecture, mechanism and modeling[J]. Chinese Journal of Computers, 2013, 36(9):1765-1784
[16] 邓维, 刘方明, 金海, 等. 云计算数据中心的新能源应用:研究现状与趋势[J]. 计算机学报, 2013, 36(3):582-598 DENG Wei, LIU Fangming, JIN Hai, et al. Leveraging renewable energy in cloud computing datacenters:state of the art and future research[J]. Chinese Journal of Computers, 2013, 36(3):582-598
[17] 王德文, 孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国电机工程学报, 2015, 35(3):527-537 WANG Dewen, SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3):527-537
[18] 张素香, 赵丙镇, 王风雨, 等. 海量数据下的电力负荷短期预测[J]. 中国电机工程学报, 2015, 35(1):37-42 ZHANG Suxiang, ZHAO Bingzhen, WANG Fengyu, et al. Short-term power load forecasting based on big data[J]. Proceedings of the CSEE, 2015, 35(1):37-42
[19] 王保义, 赵硕, 张少敏. 基于云计算和极限学习机的分布式电力负荷预测算法[J]. 电网技术, 2014, 38(2):526-531 WANG Baoyi, ZHAO Shuo, ZHANG Shaomin. A distributed load forecasting algorithm based on cloud computing and extreme learning machine[J]. Power System Technology, 2014, 38(2):526-531
[20] 刘科研, 盛万兴, 张东霞, 等. 智能配电网大数据应用需求和场景分析研究[J]. 中国电机工程学报, 2015, 35(2):287-293 LIU Keyan, SHENG Wanxing, ZHANG Dongxia, et al. Big data application requirements and scenario analysis in smart distribution network[J]. Proceedings of the CSEE, 2015, 35(2):287-293