| 引用本文: | 韩宏泉,吴珊,侯本伟.采用核极限学习机的短期需水量预测模型[J].哈尔滨工业大学学报,2022,54(2):17.DOI:10.11918/202012021 |
| HAN Hongquan,WU Shan,HOU Benwei.Short-term water demand prediction model using kernel-based extreme learning machine[J].Journal of Harbin Institute of Technology,2022,54(2):17.DOI:10.11918/202012021 |
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| 摘要: |
| 为满足给水系统日常管理对短期需水量预测时效的需求,建立了所需训练时间短的核极限学习机模型（kernel-based extreme learning machine,KELM）；从提升预测精度的角度考虑,构造了以傅里叶级数为理论依据的残差修正模块（Fourier series,FS）,利用该模块对需水量初始预测值与观测值之间的差值进行建模,完成对初始预测值的残差修正,将该模块叠加于KELM模型上形成了组合预测模型（KELM+FS）。通过实际数据对模型进行性能测试,结果表明:KELM模型能够与人工神经网络模型、支持向量回归模型产生相似的预测精度,但预测时间仅为二者平均值的5%左右；组合模型KELM+FS在未显著增加预测时间的前提下,比KELM模型相对预测精度提升了12%左右。在用于短期需水量预测时,无论单一模型KELM还是组合模型KELM+FS都能达到有效提升预测效率的目的。 |
| 关键词: 短期需水量预测 核极限学习机 组合模型 傅里叶级数 残差修正 |
| DOI：10.11918/202012021 |
| 分类号:TU991 |
| 文献标识码:A |
| 基金项目:国家水体污染控制与治理科技重大专项(2017ZX07108-002)；国家自然科学基金(51978023) |
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| Short-term water demand prediction model using kernel-based extreme learning machine |
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HAN Hongquan,WU Shan,HOU Benwei
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(Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)
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| Abstract: |
| To meet the requirements of the daily management of water supply systems for short-term water demand prediction timeliness, a kernel-based extreme learning machine model (KELM) was established, which requires short training time. From the perspective of improving the prediction accuracy, a residual correction module based on the Fourier series (FS) was constructed, which was used to model the difference between the initial predicted value and the observed value of water demand, and the residual correction of the initial predicted value was completed. The module was superimposed on the KELM model to form the hybrid prediction model (KELM+FS). The performance of the models was tested using real water demand data. Experimental results show that the KELM model could produce similar prediction accuracy as the artificial neural network model and the support vector regression model, but the prediction time was only about 5% of the average time of the two models. Compared with the KELM model, the relative prediction accuracy of the hybrid model KELM+FS was improved by about 12% without significantly increasing the prediction time. Therefore, when applied to short-term water demand prediction, both the single model KELM and the hybrid model KELM+FS could achieve the goal of improving the prediction efficiency. |
| Key words: short-term water demand prediction kernel-based extreme learning machine hybrid model Fourier series residual correction |
