基于核极限学习机的负荷多粒度预测模型

台区负荷数据不仅作为时序数据呈现自相关性,还易受台区环境因素影响呈现非平稳性,因此预测精度不仅与预测模型结构有关,还与输入数据的时序特征有关。为了提高台区负荷的预测精度,提出一种基于混沌时序分析与核极限学习机的短期负荷多粒度预测模型。针对负荷数据的非平稳特征,通过变分模态分解算法将非平稳的原始信号转换成一系列相对平稳的子信号;针对负荷数据中的自相关特征,通过混沌时序分析方法,求解各个模态输入预测模型时的时间窗大小;构建多粒度核极限学习机预测模型,解决负荷数据中非平稳、自相关性对负荷预测的不利影响,提高模型的预测精度。结果表明,负荷的预测精度受输入数据时间窗大小的影响,不同模态分量的最佳时间窗的大小不同。采用混沌相时序分析的方法评估各个模态分量的最佳时间窗大小,可以有效提升核极限学习机的预测精度。

A multi-granularity electrical load forecasting model based on kernel extreme learning machine

The load data of distribution transformer area not only exhibits autocorrelation as time-series data but also shows non-stationarity due to the influence of environmental factors. Therefore, the prediction accuracy is not only related to the structure of the prediction model but also related to the time series characteristics of the input data. In order to improve the prediction accuracy of the model, this paper proposes a multi-granularity load forecasting model based on chaotic time series analysis and kernel extreme learning machine. To deal with the non-stationary characteristics of the load data, the non-stationary original signal is converted into a series of relatively stable sub-signals through the variational modal decomposition algorithm. Regarding the autocorrelation characteristics in the load data, the chaotic time series analysis method is used to solve the size of the time window of each modal when the modal is input into the prediction model. By constructing a forecasting model based on multi-granular kernel extreme learning machine, the adverse effects of non-stationarity and autocorrelation in the load data on the load prediction are reduced, thus improving the prediction accuracy of the model. The results show that the load forecast accuracy is affected by the size of the time window of the input data, and the optimal time window for different modal components is different. The chaotic phase time series analysis method can estimate the optimal time window size of each modal component, effectively improving the prediction accuracy of the kernel extreme learning machine.