| 引用本文: | 顾熹,廖志伟.基于相空间重构和高斯过程回归的短期负荷预测[J].电力系统保护与控制,2017,45(5):73-79. |
| GU Xi,LIAO Zhiwei.Short-term load forecasting based on phase space reconstruction and Gaussian process regression[J].Power System Protection and Control,2017,45(5):73-79 |
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| 摘要: |
| 基于负荷时间序列的混沌特性，提出了一种结合相空间重构(PSR)和高斯过程回归(GPR)的短期负荷预测方法。首先采用C-C方法确定时间序列的延迟时间和嵌入维度，分别建立单变量和多变量的相空间重构模型。然后，分别运用单一与组合核函数的GP模型对负荷样本进行训练，根据最优超参数对24 h的日负荷进行预测。最后将预测结果与支持向量机模型以及多变量GP模型进行比较。结果显示，多变量组合核函数GP模型取得了更好的预测结果，验证了所提出的基于PSR和GPR的预测方法的可行性。 |
| 关键词: 相空间重构 高斯过程回归 C-C方法 短期负荷预测 组合核函数 |
| DOI：10.7667/PSPC160389 |
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| Short-term load forecasting based on phase space reconstruction and Gaussian process regression |
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GU Xi,LIAO Zhiwei
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(School of Electric Power, South China University of Technology, Guangzhou 510640, China)
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| Abstract: |
| According to the chaotic features of load series, a new forecasting method combining phase space reconstruction and Gaussian process regression is proposed. Firstly, two parameters of time series (delay time and delay window) are earned at the same time by means of the C-C method. Secondly, the reconstructed series of the separate load as well as the multi-variable model considering load and other influence factors are established. Then, the load sample is trained by GPR models using both single and composite kernel function and the optimal hyper-parameters are calculated, with which the 24-hour daily loads are predicted. Finally, the forecasting consequence of the single load model is contrasted with SVM model and the multi-variable GP model. Prediction results indicate that the model using multi-variable and composite kernel function achieves better effects and the new method is not only feasible but also satisfies the requirements of the engineering precision. |
| Key words: phase space reconstruction Gaussian process regression C-C method short-term load forecasting composite kernel function |
