This paper introduces the methods and the steps of predicting the power load by the BP neural network with cluster optimization in batch processing time series. Through the preconditioning of historical data, the setting of the initial clustering center and the determination of the optimal number of clusters, a clustering prediction model of the load curve is established based on the clustering results of the historical data and the relevant parameters such as the temperature, the humidity, the air pressure, the wind speed and the time (the current week). The results show that with the clustering algorithm, the related factors and the BP network adaptive rate can be comprehensively considered, while the training speed is improved, to obtain more accurate prediction results.
[1] 陈宏义, 李存斌, 施立刚. 基于聚类分析的短期负荷智能预测方法研究[J]. 湖南大学学报(自然科学版), 2014, 41(5):94-98. Chen Hongyi, Li Cunbin, Shi Ligang. A new forecasting approach for short-term load intelligence base on cluster method[J]. Journal of Hu-nan Universiter(Natural Sciences), 2014, 41(5):94-98.
[2] 张素香, 刘建明, 赵丙镇, 等. 基于云计算的居民用电行为分析模型研究[J]. 电网技术, 2013, 37(6):1542-1546. Zhang Suxiang, Liu Jianming, Zhao Bingzhen, et al. Cloud computingbased analysis on residential electricity consumption behavior[J]. Power System Technology, 2013, 37(6):1542-1546.
[3] 张璨, 王蓓蓓, 李扬. 典型行业用户需求响应行为研究[J]. 华东电力, 2012, 40(10):1701-1705. Zhang Can, Wang Beibei, Li Yang. Demand response behavior of typi-cal industrial users[J]. East China Electric Power 2012, 40(10):1701-1705.
[4] 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 forecating based on improved random forest with grey relation pro-jection[J]. Automation of Electric Power Systems, 2015, 39(12):50-55.
[5] 朱健峥. 短期台区电力负荷预测方法探讨[J]. 机电信息, 2010(18):120-121. Zhu Jianzheng. Discussion on short term power load forecasting method in taiqu[J]. Electromechanical Information, 2010(18):120-121.
[6] 张晓虎, 罗隆福, 刘洁. 基于负荷实测的配电网无功优化及其降损节能效益分析[J]. 电网技术, 2012, 36(5):226-231. Zhang Xiaohu, Luo Longfu, Liu Jie. Load measurement based reactive power optimization for distribution network and analysis on its effects in loss reduction and energy conservation[J]. Power System Technology, 2012, 36(5):226-231.
[7] 王新, 孟玲玲. 基于EEMD-LSSVM的超短期负荷预测[J]. 电力系统保护与控制, 2015, 43(1):61-66. Wang Xin, Meng Lingling. Ultra-short-term load forecasting based on EEMD-LSSVM[J]. Power System Protection and Control, 2015, 43(1):61-66.
[8] Tucci M, Crisostomi E, Giunta G, et al. A multi-objective method for short-term load forecasting in European countries[J]. IEEE Transac-tions on Power Systems, 2016, 31(5):3537-3547.
[9] 张刚, 刘福潮, 王维洲, 等. 电网短期负荷预测的BP-ANN方法及应用[J]. 电力建设, 2014, 35(3):54-58. Zhang Gang, Liu Fuchao, Wang Weizhou, et al. BP-ANN Method for power grid short-term load forecasting and its application[J]. Power Construction, 2014, 35(3):54-58.
[10] 刘念, 张清鑫, 刘海涛. 基于核函数极限学习机的微电网短期负荷预测方法[J]. 电工技术学报, 2015, 30(8):218-224. Liu Nian, Zhang Qingxin, Liu Haitao. Online short-term load forecast-ing based on ELM with kernel algorithm in micro-grid environment[J]. Transactions of China Electrotechnical Society, 2015, 30(8):218-224.
[11] Ul Islam B, Baharudin Z, Nallagownden P. Short term electric load forecasting with back propagation neural network and simulated an-nealing genetic algorithm[J]. Applied Mechanics & Materials, 2015, 785:14-18.
[12] 金义雄, 段建民, 徐进, 等. 考虑气象因素的相似聚类短期负荷组合预测方法[J]. 电网技术, 2007, 31(19):60-64. Jin Yixiong, Duan Jianmin, Xu Jin, et al. A combinational short-term load forecasting method by use of similarity clustering and consider-ing weather factors[J]. Power System Technology, 2007, 31(19):60-64.
[13] Duan Q H, Liu J R, Zhao D F. Short term electric load forecasting us-ing an automated system of model choice[J]. International Journal of Electrical Power & Energy Systems, 2017, 91:92-100.
[14] Baliyan A, Gaurav K, Mishra S K. A review of short term load forecast-ing using artificial neural network models[J]. Procedia Computer Sci-ence, 2015, 48:121-125.
[15] 康重庆, 周安石, 王鹏, 等. 短期负荷预测中实时气象因素的影响分析及其处理策略[J]. 电网技术, 2006(7):5-10. Kang Chongqing, Zhou Anshi, Wang Peng, et al. Impact analys is of hourly weather facotsr in short-term load foercasting and its porcess-ing startegy[J]. Power System Technology, 2006(7):5-10.
[16] 方芳, 田世明, 卜凡鹏, 等. 一种自适应选择样本的用电负荷预测方法[J]. 电子技术应用, 2017, 43(11):18-21, 26. Fang Fang, Tian Shiming, Bu Fanpeng, et al. An electric load fore-casting method based on adaptive selection of samples[J]. Application of Electronic Technique, 2017, 43(11):18-21, 26.
[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):527-536. Wang Dewen, Sun Zhiwei. Big data analysis and parallel load forecast-ing of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3):527-536.
