基于粗糙集-混沌时间序列Elman神经网络的短期用电量预测
Elman神经网络由于其具有无限逼近和适应时变特性的能力被广泛用于动态数据预测.短期的用电量存在多种不确定影响因素, 为了将所有影响因素考虑其中, 引入混沌时间序列的重构相空间技术.由于神经网络在非线性函数中对于峰值预测偏差较大, 粗糙集理论可以对其做出修正.因此, 引入混沌时间序列理论和粗糙集理论改进Elman神经网络并进行建模.模型应用嵌入维度和延迟时间重构相空间恢复原来系统的动力学形态, 将处理好的数据代入Elman神经网络进行用电量预测.最后引入粗糙集修正误差较大的峰值点, 提高预测精度.收集了Heriot-Watt大学某宿舍楼30天的用电量数据, 以5 min为计数频率共8 640个...
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| Published in | 电力系统保护与控制 Vol. 47; no. 3; pp. 23 - 30 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
海南大学,海南 海口,570228%赫瑞·瓦特大学,爱丁堡 EH14 4AS
01.02.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
| DOI | 10.7667/PSPC180274 |
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| Abstract | Elman神经网络由于其具有无限逼近和适应时变特性的能力被广泛用于动态数据预测.短期的用电量存在多种不确定影响因素, 为了将所有影响因素考虑其中, 引入混沌时间序列的重构相空间技术.由于神经网络在非线性函数中对于峰值预测偏差较大, 粗糙集理论可以对其做出修正.因此, 引入混沌时间序列理论和粗糙集理论改进Elman神经网络并进行建模.模型应用嵌入维度和延迟时间重构相空间恢复原来系统的动力学形态, 将处理好的数据代入Elman神经网络进行用电量预测.最后引入粗糙集修正误差较大的峰值点, 提高预测精度.收集了Heriot-Watt大学某宿舍楼30天的用电量数据, 以5 min为计数频率共8 640个计数点作为数据集进行预测仿真.预测结果与Elman神经网络和混沌时间序列Elman神经网络进行对比, 验证了该模型在短时间预测的有效性. |
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| AbstractList | Elman神经网络由于其具有无限逼近和适应时变特性的能力被广泛用于动态数据预测.短期的用电量存在多种不确定影响因素, 为了将所有影响因素考虑其中, 引入混沌时间序列的重构相空间技术.由于神经网络在非线性函数中对于峰值预测偏差较大, 粗糙集理论可以对其做出修正.因此, 引入混沌时间序列理论和粗糙集理论改进Elman神经网络并进行建模.模型应用嵌入维度和延迟时间重构相空间恢复原来系统的动力学形态, 将处理好的数据代入Elman神经网络进行用电量预测.最后引入粗糙集修正误差较大的峰值点, 提高预测精度.收集了Heriot-Watt大学某宿舍楼30天的用电量数据, 以5 min为计数频率共8 640个计数点作为数据集进行预测仿真.预测结果与Elman神经网络和混沌时间序列Elman神经网络进行对比, 验证了该模型在短时间预测的有效性. |
| Abstract_FL | Elman neural network is widely used for dynamic data prediction because of its ability to approximate and adapting to time-varying characteristics. There are many uncertain factors in the short-term electricity consumption. In order to take all the factors into account, this paper introduces the reconstruction phase space technology of chaotic time series. Due to the large deviation from the neural network of the peak prediction in nonlinear functions, it can be modified by rough set theory. Therefore, the chaotic time series theory and rough set theory are introduced to improve the Elman neural network. The model applies embedded dimension and delay time to reconstruct the phase space to restore the original system's dynamic morphology. The processed data is brought into the Elman neural network to predict the electricity consumption. Finally, the peak point corrected by the rough set is introduced to improve the prediction accuracy. This paper collects the data from a dormitory building in Heriot-Watt university of Edinburgh. It uses thirty days electricity data with 8 640 points as the data set to do predict simulation. The prediction results are compared with the Elman neural network and chaotic time series Elman neural network, and the validity of the model are verified in a short-time prediction. |
| Author | 李艳 吴佳懋 符一健 |
| AuthorAffiliation | 海南大学,海南 海口,570228%赫瑞·瓦特大学,爱丁堡 EH14 4AS |
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| Author_FL | WU Jiamao LI Yan FU Yijian |
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| Keywords | 混沌时间序列 用电量预测 prediction of electricity consumption 粗糙集 rough set Elman神经网络 chaotic time series Elman neural network |
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| PublicationTitle_FL | Power System Protection and Control |
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| Publisher | 海南大学,海南 海口,570228%赫瑞·瓦特大学,爱丁堡 EH14 4AS |
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| Snippet | Elman神经网络由于其具有无限逼近和适应时变特性的能力被广泛用于动态数据预测.短期的用电量存在多种不确定影响因素, 为了将所有影响因素考虑其中, 引入混沌时间序列的重构相空间技术.由于神经网络在非线性函数中对于峰值预测偏差较大, 粗糙集理论可以对其做出修正.因此,... |
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| Title | 基于粗糙集-混沌时间序列Elman神经网络的短期用电量预测 |
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