Short-Term Load Forecasting Based on Similar-Day Selection with GRA-K-means

doi:10.12204/j.issn.1000-7229.2021.07.013

Abstract

Abstract:

As the selection of similar days affecting the accuracy of load forecasting, a new short-term load forecasting model using GRA-K-means to select similar days is proposed in this paper. Firstly, GRA is used to select similar days to obtain rough sets of similar days. Secondly, K-means is used to cluster the external factors of rough sets of similar days, and then calculate the Euclidean distance between the predicated days and cluster center. The minimum distance is regarded as the final similar day set. Finally, the final similar day set is applied to train LSTM forecast model of neutral network to load forecast. Compared with LSTM model without similar days or LSTM model using traditional GRA, the MAPE in this paper is partly reduced by 0.911% and 0.637%. The analysis of the results shows that the model using GRA-K-means to select similar days can effectively improve the accuracy of short-term load forecasting.

Key words: short-term load forecasting, grey relation anlysis(GRA), K-means cluster, similar days, long-short term memory(LSTM) neural networks

CLC Number:

TM715

HUANG Dongmei, ZHUANG Xingke, HU Anduo, SUN Jinzhong, SHI Shuai, SUN Yuan, TANG Zhen. Short-Term Load Forecasting Based on Similar-Day Selection with GRA-K-means[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 110-117.

Figures/Tables 13

Fig.1 Variations of the maximum temperature and total daily load in a certain area in the first half of 2014

Table 1 Date type encoding

Table 2 Correlation coefficients of influencing factors on similar days

Fig.2 Basic unit of LSTM neural network

Fig.3 Diagram of the model frame

Table 3 Input features of the model

Fig.4 EEEEEEevaluation index of clustering

Fig.5 Comprehensive evaluation index of clustering

Table 4 Euclidean distance between cluster center and the day to be predicted

Table 5 Average of shape coefficient

Table 6 Error comparison of different similar-day selection methods

Fig.6 Load forecasting curves of different similar-day search methods

Table 7 Error comparison of different models

References 29

[1]	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.
[2]	ZHANG P, WU X Y, WANG X J, et al. Short-term load forecasting based on big data technologies[J]. CSEE Journal of Power and Energy Systems, 2015, 1(3):59-67. doi: 10.17775/CSEEJPES.2015.00036 URL
[3]	ZHENG H T, YUAN J B, CHEN L. Short-term load forecasting using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation[J]. Energies, 2017, 10(8):1168. doi: 10.3390/en10081168 URL
[4]	焦润海, 苏辰隽, 林碧英, 等. 基于气象信息因素修正的灰色短期负荷预测模型[J]. 电网技术, 2013, 37(3):720-725.
	JIAO Runhai, SU Chenjun, LIN Biying, et al. Short-term load forecasting by grey model with weather factor-based correction[J]. Power System Technology, 2013, 37(3):720-725.
[5]	CAPRIO U D, GENESIO R, POZZI S, et al. Short term load forecasting in electric power systems: A comparison of ARMA models and extended Wiener filtering[J]. Journal of Forecasting, 1983, 2(1):59-76. doi: 10.1002/(ISSN)1099-131X URL
[6]	HOUIMLI R, ZMAMI M, BEN-SALHA O. Short-term electric load forecasting in Tunisia using artificial neural networks[J]. Energy Systems, 2020, 11(2):357-375. doi: 10.1007/s12667-019-00324-4 URL
[7]	宫毓斌, 滕欢. 基于GOA-SVM的短期负荷预测[J]. 电测与仪表, 2019, 56(14):12-16.
	GONG Yubin, TENG Huan. Short-term load forecasting based on GOA-SVM[J]. Electrical Measurement & Instrumentation, 2019, 56(14):12-16.
[8]	王义军, 李殿文, 高超, 等. 基于改进的PSO-SVM的短期电力负荷预测[J]. 电测与仪表, 2015, 52(3):22-25.
	WANG Yijun, LI Dianwen, GAO Chao, et al. Short-term power load forecasting based on improved PSO-SVM[J]. Electrical Measurement & Instrumentation, 2015, 52(3):22-25.
[9]	赵佩, 代业明. 基于实时电价和加权灰色关联投影的SVM电力负荷预测[J]. 电网技术, 2020, 44(4):1325-1332.
	ZHAO Pei, DAI Yeming. Power load forecasting of SVM based on real-time price and weighted grey relational projection algorithm[J]. Power System Technology, 2020, 44(4):1325-1332.
[10]	赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12):4370-4376.
	ZHAO Bing, WANG Zengping, JI Weijia, et al. A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power System Technology, 2019, 43(12):4370-4376.
[11]	李鹏, 何帅, 韩鹏飞, 等. 基于长短期记忆的实时电价条件下智能电网短期负荷预测[J]. 电网技术, 2018, 42(12):4045-4052.
	LI Peng, HE Shuai, HAN Pengfei, et al. Short-term load forecasting of smart grid based on long-short-term memory recurrent neural networks in condition of real-time electricity price[J]. Power System Technology, 2018, 42(12):4045-4052.
[12]	邹政达, 孙雅明, 张智晟. 基于蚁群优化算法递归神经网络的短期负荷预测[J]. 电网技术, 2005, 29(3):59-63.
	ZOU Zhengda, SUN Yaming, ZHANG Zhisheng. Short-term load forecasting based on recurrent neural network using ant colony optimization algorithm[J]. Power System Technology, 2005, 29(3):59-63.
[13]	彭文, 王金睿, 尹山青. 电力市场中基于Attention-LSTM的短期负荷预测模型[J]. 电网技术, 2019, 43(5):1745-1751.
	PENG Wen, WANG Jinrui, YIN Shanqing. Short-term load forecasting model based on attention-LSTM in electricity market[J]. Power System Technology, 2019, 43(5):1745-1751.
[14]	陆继翔, 张琪培, 杨志宏, 等. 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(8):131-137.
	LU Jixiang, ZHANG Qipei, YANG Zhihong, et al. Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43(8):131-137.
[15]	陈振宇, 刘金波, 李晨, 等. 基于LSTM与XGBoost组合模型的超短期电力负荷预测[J]. 电网技术, 2020, 44(2):614-620.
	CHEN Zhenyu, LIU Jinbo, LI Chen, et al. Ultra short-term power load forecasting based on combined LSTM-XGBoost model[J]. Power System Technology, 2020, 44(2):614-620.
[16]	王瑞, 闫方, 逯静, 等. 运用相似日和LSTM的短期负荷双向组合预测[J/OL]. 电力系统及其自动化学报. ( 2020-11-16)[2021-01-11]. https://doi.org/10.19635/j.cnki.csu-epsa.000671.
	WANG Rui, YAN Fang, LU Jing, et al. Bidirectional Combined Short-term Load Forecasting by Using Similar Days and LSTM[J/OL]. Proceedings of the CSU-EPSA. ( 2020-11-16)[2021-01-11]. https://doi.org/10.19635/j.cnki.csu-epsa.000671.
[17]	李闯, 孔祥玉, 朱石剑, 等. 能源互联环境下考虑需求响应的区域电网短期负荷预测[J]. 电力系统自动化, 2021, 45(1):71-78.
	LI Chuang, KONG Xiangyu, ZHU Shijian, et al. Short-term load forecasting of regional power grid considering demand response in energy interconnection environment[J]. Automation of Electric Power Systems, 2021, 45(1):71-78.
[18]	吴潇雨, 和敬涵, 张沛, 等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测[J]. 电力系统自动化, 2015, 39(12):50-55.
	WU Xiaoyu, HE Jinghan, ZHANG Pei, et al. Power system short-term load forecasting based on improved random forest with grey relation projection[J]. Automation of Electric Power Systems, 2015, 39(12):50-55.
[19]	谷云东, 马冬芬, 程红超. 基于相似数据选取和改进梯度提升决策树的电力负荷预测[J]. 电力系统及其自动化学报, 2019, 31(5):64-69.
	GU Yundong, MA Dongfen, CHENG Hongchao. Power load forecasting based on similar-data selection and improved gradient boosting decision tree[J]. Proceedings of the CSU-EPSA, 2019, 31(5):64-69.
[20]	刘田梦, 王丽婕, 马嫒. 基于聚类分析与神经网络的电力系统负荷预测[J]. 北京信息科技大学学报(自然科学版), 2018, 33(4):24-28.
	LIU Tianmeng, WANG Lijie, MA Ai. Power system load forecasting based on clustering analysis and neural network[J]. Journal of Beijing Information Science & Technology University, 2018, 33(4):24-28.
[21]	陈鸿琳. 基于相似日和智能算法的短期负荷组合预测[D]. 长沙: 湖南大学, 2016.
	CHEN Honglin. Short-term load combination forecast model based on similar days and intelligent algorithms[D]. Changsha: Hunan University, 2016.
[22]	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.
	WU Yun, LEI Jianwen, BAO Lishan, et al. Short-term load forecasting based on improved grey relational analysis and neural network optimized by bat algorithm[J]. Automation of Electric Power Systems, 2018, 42(20):67-72.
[23]	席雅雯, 吴俊勇, 石琛, 等. 融合历史数据和实时影响因素的精细化负荷预测[J]. 电力系统保护与控制, 2019, 47(1):80-87.
	XI Yawen, WU Junyong, SHI Chen, et al. A refined load forecasting based on historical data and real-time influencing factors[J]. Power System Protection and Control, 2019, 47(1):80-87.
[24]	GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]//2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada: IEEE, 2013: 6645-6649.
[25]	李滨, 陆明珍. 考虑实时气象耦合作用的地区电网短期负荷预测建模[J]. 电力系统自动化, 2020, 44(17):60-68.
	LI Bin, LU Mingzhen. Short-term load forecasting modeling of regional power grid considering real-time meteorological coupling effect[J]. Automation of Electric Power Systems, 2020, 44(17):60-68.
[26]	刘思, 李林芝, 吴浩, 等. 基于特性指标降维的日负荷曲线聚类分析[J]. 电网技术, 2016, 40(3):797-803.
	LIU Si, LI Linzhi, WU Hao, et al. Cluster analysis of daily load curves using load pattern indexes to reduce dimensions[J]. Power System Technology, 2016, 40(3):797-803.
[27]	赵文清, 龚亚强. 基于Kernel K-means的负荷曲线聚类[J]. 电力自动化设备, 2016, 36(6):203-207.
	ZHAO Wenqing, GONG Yaqiang. Load curve clustering based on Kernel K-means [J]. Electric Power Automation Equipment, 2016, 36(6):203-207.
[28]	CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016: 785-794.
[29]	许佳辉, 王向文, 杨俊杰. 基于CNN-QRLightGBM的短期负荷概率密度预测[J]. 电网技术, 2020, 44(9):3409-3416.
	XU Jiahui, WANG Xiangwen, YANG Junjie. Short-term load density prediction based on CNN-QRLightGBM[J]. Power System Technology, 2020, 44(9):3409-3416.