基于VMD-SSA-BiLSTM的多维时序电力负荷预测

Multi-featured power load forecasting based on VMD-SSA-BiLSTM

中文关键词: 负荷预测  变分模态分解  麻雀搜索  神经网络

英文关键词: load forecasting  variational mode decomposition  sparrow search algorithm  neural network

中文摘要:

      为了全面发掘负荷数据中的时序信息和天气信息,以提高电力负荷预测的精准度,提出一种基于变分模态分解（variation-al mode decomposition,VMD）和双向长短期记忆神经网络（bi-direction long short-term memory,BiLSTM）相耦合的多维时序电力负荷预测方法。首先通过对多维天气信息和时序信息进行相关性分析,选取相关性高的特征向量作为输入,同时利用VMD将原始负荷数据分解为不同频率的本征模态函数（intrinsic mode function,IMF）,然后将IMF和相关性高的特征向量输入到经过麻雀搜索算法（sparrow search algorithm,SSA）优化的BiLSTM神经网络进行预测,最后叠加IMF的预测值,得到最终电力负荷预测值,并进行实例验证。与BiLSTM、VMD-BiLSTM模型相比,VMD-SSA_BiLSTM模型能够充分挖掘数据中的时序信息和天气信息,可以提升多维负荷数据的预测精度。

英文摘要:

      To fully explore the timing and weather information in load data and improve the accuracy of power load prediction, a neural network based on variational mode decomposition（VMD）and bi-directional long short-term memory（BiLSTM）is proposed. Multi-dimensional sequential power load forecasting method leverages the strengths of VMD and BiLSTM to improve the accuracy of power load prediction. Firstly, through correlation analysis of multi-dimensional weather information and time sequence information, feature vectors with high correlation are selected as inputs. Meanwhile, VMD is used to decompose the original load data into intrinsic mode functions（IMF）of different frequencies. Then, the IMF and feature vector with high correlation are input to BiLSTM neural network optimized by sparrow search algorithm（SSA）for prediction. Finally, the predicted value of IMF is superimposed to obtain the final predicted value of power load. Load forecasting data set of 2016 electrical mathematical contest in modeling is used as an example to verify. Compared with BiLSTM and VMD-BiLSTM model, VMD-SSA-BiLSTM model can fully mine timing and weather information in data, and improve the prediction accuracy of multidimensional load data.