基于集成深度学习算法的燃气负荷预测方法

Gas Load Forecasting Method Based on Integrated Deep Learning Algorithms

燃气负荷预测受到社会经济、天气因素、日期类型等多种复杂因素的影响,而多因素的共同作用则必然会导致燃气负荷序列变化趋势具有很大的随机性以及一定程度上的复杂性.为了有效提高燃气负荷预测的精度,本文提出了一种新型的集成深度算法来对燃气负荷进行多步预测.首先通过EEMD算法将非平稳非线性的负荷序列分解为若干个稳态且线性的本征模式分量及剩余项,有效的避免了传统EMD带来的模态混叠问题,然后将负荷数据的影响因素输入到AutoEncoder中进行特征提取并做非线性降维处理,再将EEMD分解得到的每个子序列分别与AutoEncoder提取到的特征序列组成不同的训练矩阵,最后针对不同的子序列对应的训练矩阵建立相应的LSTM预测模型,重构分量预测值得到最终预测结果.为了验证所提出算法的有效性和预测性能,使用上海燃气数据来进行上述模型的仿真实验,结果证明相较对比方法,预测精度有了明显的提高.

Gas load forecasting is affected by various complex factors such as social economy,weather factors,date types,and the combination of multiple factors,and it will inevitably lead to a large randomness and a certain degree of complexity in the trend of gas load sequence changes.In order to effectively improve the accuracy of gas load forecasting,a new integrated deep learning algorithms is proposed to predict the gas load in multiple steps.Firstly,the non-stationary nonlinear load sequence is decomposed into several steady-state and linear IMF components and residuals by the set of EEMD algorithm,which effectively avoids the modal aliasing problem caused by the traditional EMD.Then,each subsequence obtained by EEMD decomposition is composed of a training matrix different from the feature sequence extracted by AutoEncoder.After that,each subsequence obtained by EEMD decomposition is composed of a training matrix different from the feature sequence extracted by AutoEncoder.Finally,the corresponding Long Short Term Memory(LSTM)prediction model is established for the training matrices corresponding to different subsequences,and the component prediction values are reconstructed to obtain the final prediction result.In order to verify the effectiveness and prediction performance of the proposed algorithm,the Shanghai gas data was used to simulate the above model.The results show that the prediction accuracy is significantly improved compared with the comparison method.