基于SSA非降噪处理的电力负荷预测

Yindong Jin, He Xiao, Chengui Fu
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引用次数: 0

摘要

针对电力负荷数据随机性大、预测精度低的问题,将奇异谱分析(SSA)与加特征映射层的门控循环单元(GRU)网络相结合,形成电力负荷预测模型,有效提高了电力负荷预测精度。该方法以历史负荷数据为输入,采用非线性时间序列处理技术SSA提取反映负荷复杂动态变化的特征,将提取的特征向量构造成时间序列形式作为FL-GRU网络的输入,并将各子序列的预测结果进行叠加。获取最终预测结果。为了避免在降噪过程中丢失数据中的有效信息,本方法进行非降噪处理。用英国家庭用电负荷数据集和ISO新英格兰提供的数据集进行了实验,该方法在两个数据集上的预测准确率分别达到了98.86%和97.31%。
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Power load forecasting based on SSA non-noise reduction processing
Aiming at the problems of high randomness and low prediction accuracy of power load data, a power load prediction model is formed by integrating Singular Spectrum Analysis(SSA) and a Gated Recurrent Unit(GRU) network with a feature mapping layer added, which can effectively improve the power load prediction accuracy. The method takes historical load data as input, uses nonlinear time series processing technology SSA to extract features reflecting complex dynamic changes of load, constructs the extracted feature vector into a time series form as the input of the FL-GRU network, and superimposes the prediction results of each subsequence. get the final prediction result. To avoid the loss of effective information in the data during the noise reduction process, the method performs non-noise reduction processing. Experiments are carried out with a household power load data set in the UK and a data set provided by ISO New England, the method achieved 98.86% and 97.31% prediction accuracy on both datasets.
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