集成自适应啁啾模态分解和BiLSTM的短期负荷组合预测模型

为提高用户侧短期负荷预测的精度，提出了一种基于自适应啁啾模态分解(adaptive chirp mode decomposition, ACMD)和麻雀搜索算法(sparrow search algorithm, SSA)优化双向长短时记忆网络(bi-directional long short-term memory, BiLSTM)的短期负荷组合预测方法。针对短期电力负荷存在波动性强和非平稳性的问题，采用ACMD将短期负荷时间序列分解为多个相对简单的子分量，使用BiLSTM分别对各子分量进行预测。同时，为克服BiLSTM参数取值不同导致预测结果不稳定的问题，使用SSA优化BiLSTM模型的超参数。最后将各子分量预测结果叠加得到最终预测结果。通过具体算例，分别与单一预测模型和多种组合预测模型进行比较，实验结果表明该方法具有更高的预测精度。

Short-term load combination forecasting model integrating ACMD and BiLSTM

To improve the accuracy of short-term load forecasting on the user side, a short-term load combination prediction method based on adaptive chirp mode decomposition (ACMD) and sparrow search algorithm (SSA) optimized bi-directional long short-term memory network (BiLSTM) is proposed. Given the problem of strong fluctuation and non-stationarity of short-term power load, ACMD is used to decompose the short-term load time series into several relatively simple sub-components, and BiLSTM is used to predict each sub-component. At the same time, in order to overcome the problem of unstable prediction results caused by different parameter values of BiLSTM, SSA is used to optimize the hyperparameters of the BiLSTM model. The prediction results of each sub-component are superimposed to obtain the final prediction results. Compared with single prediction model and multiple combination prediction models, the experimental results show that this method has higher prediction accuracy. This work is supported by the National Science Foundation of China (No. 52077146).