二次模态分解组合DBiLSTM-MLR的综合能源系统负荷预测

用户级综合能源系统多元负荷存在随机性、波动性相对更大的特点,现有预测方法不能得到很好的预测效果.为此提出一种基于核主成分分析(KPCA)、二次模态分解、深度双向长短期记忆(DBiLSTM)神经网络和多元线性回归(MLR)的多元负荷预测模型.首先,运用自适应噪声的完全集合经验模态分解分别对电、冷、热负荷进行本征模态分解,对分解得到的强非平稳分量运用变分模态分解进行再次分解.然后,运用KPCA对天气、日历规则特征集提取主成分实现数据降维;将分解得到的非平稳、平稳分量结合特征集主成分分别用DBiLSTM神经网络、MLR进行预测.最后,将预测结果进行重构得到最终预测结果.通过实际算例分析可知,与其他模型相比,所提模型具有更高的预测精度.

Load Prediction of Integrated Energy System Based on Combination of Quadratic Modal Decomposition and Deep Bidirectional Long Short-term Memory and Multiple Linear Regression

Due to the features of relatively greater randomness and volatility of multiple loads in the user-level integrated energy system, the existing prediction methods cannot get good prediction effects. To this end, a multiple load prediction model based on kernel principal component analysis (KPCA), quadratic modal decomposition, deep bidirectional long short-term memory (DBiLSTM) neural network and multiple linear regression (MLR) is proposed. First, the complete ensemble empirical mode decomposition with noise adaptability is used to perform eigenmode decomposition of the electric, cooling, and heating loads, and the obtained strong non-steady components after decomposition are decomposed again by using variational modal decomposition. Then, KPCA is used to extract principal components from feature sets of weather and calendar rules to achieve data dimension reduction. The decomposed non-steady and steady components, combined with the principal components of the feature set are respectively predicted by DBiLSTM neural network and MLR. Finally, the prediction results are reconstructed to obtain the final prediction results. Through the analysis of actual calculation examples and compared with other models, the proposed model has higher prediction accuracy.