| 短期电力负荷时间序列预测的极限学习机方法 |
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| 引用本文： | 张宁.短期电力负荷时间序列预测的极限学习机方法[J].福建工程学院学报,2017,0(4):367-370. |
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| 作者姓名： | 张宁 |
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| 作者单位： | 闽江学院物理与电子信息工程系物理与电子信息工程系 |
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| 摘 要： | 提出将一种进化的神经网络模型——极限学习机应用于短期电力负荷时间序列预测中，该方法具有模型参数设置少、训练速度快和良好的泛化能力等明显优点。通过实例分析表明该模型的预测精度要优于BP神经网络模型，同时也验证了该模型应用于短期负荷预测的有效性和可行性。
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| 关 键 词： | 极限学习机 BP神经网络模型 短期电力负荷 时间序列 |
Extreme learning machine method for short-term power load time series forecasting |
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| Zhang Ning.Extreme learning machine method for short-term power load time series forecasting[J].Journal of Fujian University of Technology,2017,0(4):367-370. |
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| Authors: | Zhang Ning |
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| Institution: | Physics & Electronic Information Engineering Department, Minjiang University |
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| Abstract: | The utilization of extreme learning machine—an evolution model of neural networks in short-term power load time series forecasting was proposed, which has the advantages of less parameter setting, fast training speed and better generalization ability. The validity and feasibility of the model were verified. The simulation results indicate that the forecasting accuracy of the model is better than that of BP neural network model. |
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| Keywords: | extreme learning machine BP neural network model short-term power load time series forecasting |
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