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| 提高时间序列气象适应性的短期电力负荷预测算法 | |
| 引用本文： | 朱陶业,李应求,张颖,张学庄,何朝阳.提高时间序列气象适应性的短期电力负荷预测算法[J].中国电机工程学报,2006,26(23):14-19. |
| 作者姓名： | 朱陶业 李应求 张颖 张学庄 何朝阳 |
| 作者单位： | 1. 中南大学信息物理工程学院,湖南省,长沙市410083 2. 长沙理工大学,湖南省,长沙市410077 3. 广西电力有限公司电网调度中心,广西壮族自治区,南宁市,530023 |
| 基金项目： | 国家自然科学基金项目(10471012)。~~ |
| 摘 要： | 采用时间序列中的自回归求和移动平均算法(ARIMA)对日负荷进行粗预测,获得消除了周期性的受气象因素影响较强的差值序列。结合气象信息,为小规模神经网络构造能反映气象变化的新息序列,为网络提供良好的训练与适应环境,训出对气象非平稳变化敏感的输出因子Y,再用敏感因子对ARIMA算法的预测结果进行修正,从而构建出对气象适应性较强的ARIMA Y的预测算法。利用Delphi5．0实现的负荷预测软件对广西负荷区进行预测,多年的运行证明：该算法对广西负荷区气象非平稳变化具有很好的敏感性和适应性,能显著提高气象非平稳变化日的预测准确率,较好地解决了在气象变化影响下用ARIMA算法预测准确率偏低的问题。 |
| 关 键 词： | 电力系统 负荷预测 时间序列 神经网络 气象敏感因子 |
| 文章编号： | 0258-8013（2006）23-0014-06 |
| 收稿时间： | 2006-06-21 |
| 修稿时间： | 2006年6月21日 |
A New Algorithm of Advancing Weather Adaptability Based on Arima Model for Day-ahead Power Load Forecasting |
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| ZHU Tao-ye,LI Ying-qiu,ZHANG Ying,ZHANG Xue-zhuang,HE Chao-yang.A New Algorithm of Advancing Weather Adaptability Based on Arima Model for Day-ahead Power Load Forecasting[J].Proceedings of the CSEE,2006,26(23):14-19. | |
| Authors: | ZHU Tao-ye LI Ying-qiu ZHANG Ying ZHANG Xue-zhuang HE Chao-yang |
| Affiliation: | 1. School of Info Physics and Geomatics Engineering, Central South University, Changsha 410083, Hunan Province, China; 2. Changsha University of Science Technology, Changsha 410077, Hunan Province, China; 3. Guangxi Electric Power Corporation Electric Network Control Center, N anning 530023, Guangxi Zhuang Autonpmous, China |
| Abstract: | The load forecasting accuracy is lower with ARIMA algorithm when weather change is unstable.Modeling methods of sparse coefficient ARIMA and sensitive BPNN to weather change are expounded in this paper.The ARIMA algorithm is used to forecast load and generate the sensitive error sequence that periodicity was removed to weather change,and this error and weather information are used to create innovation sequence and build training sample sets for small-scale BPNN and supply nicer training and acclimation for BPNN,obtaining sensitive output factor to unstable weather change in Guangxi load area.The forecasting software has been achieved and applied in Guangxi many years.The results show that the ARIMA algorithm with this adjustment factor can availably improve the forecasting precision,which precedes national standard. |
| Keywords: | power system power load forecasting time series back propagation neural networks sensitive weather factor |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |