| 混合属性约简方法在电力负荷预测中的应用 |
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| 引用本文： | 常玉慧,钱进,郭庆军.混合属性约简方法在电力负荷预测中的应用[J].计算机工程与应用,2011,47(30):234-236. |
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| 作者姓名： | 常玉慧 钱进 郭庆军 |
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| 作者单位： | 江苏技术师范学院 计算机工程学院，江苏 常州 213001 |
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| 基金项目： | 江苏省省属高校自然科学资金项目(No.09KJD520004) |
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| 摘 要： | 影响电力短期负荷预测精度的因素众多，为了找到负荷值与各种外在因素之间的关系，提出了一种基于粗糙集理论的混合属性约简算法，并对与预测日相似性数据进行快速约简，讨论了基于混合属性约简和BP神经网络相结合的预测模型。实验结果表明，这种方法提高了短期电力负荷预测精度。
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| 关 键 词： | 混合属性约简算法 粗糙集 BP神经网络 短期负荷预测 |
| 修稿时间： | |
Hybrid attribute reduction method and its application in power load forecasting |
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| CHANG Yuhui,QIAN Jin,GUO Qingjun.Hybrid attribute reduction method and its application in power load forecasting[J].Computer Engineering and Applications,2011,47(30):234-236. |
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| Authors: | CHANG Yuhui QIAN Jin GUO Qingjun |
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| Affiliation: | College of Computer Engineering，Jiangsu Teachers University of Technology，Changzhou，Jiangsu 213001，China |
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| Abstract: | There are many factors that influence the accuracy of short power load forecasting.In order to find the relationship between the load value and the outside factors,this paper presents a fast hybrid attribute reduction algorithm for data reduction based on rough set,and then discusses the forecasting model using hybrid attribute reduction and the BP artificial neural network.The experiment results show the model improves the forecasting accuracy. |
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| Keywords: | hybrid attribute reduction algorithm rough set BP artificial neural network short power load forecasting
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