针对光伏发电功率存在随机波动性的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化长短期记忆(long short term memory,LSTM)神经网络的短期光伏发电...针对光伏发电功率存在随机波动性的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化长短期记忆(long short term memory,LSTM)神经网络的短期光伏发电功率预测方法。首先,通过VMD算法将多维光伏特征数据分解为若干不同频率的本征模态和残差分量,以降低原始序列的非平稳性;然后,采用ISSA对LSTM神经网络超参数进行全局寻优,建立了不同模态序列分量下的ISSA-LSTM组合模型;最后,使用训练好的组合模型对各分解的子序列模态特征分量进行多维预测,并将各层模态预测序列叠加组合成最终的输出结果。仿真结果表明,构建的VMD-ISSA-LSTM组合模型相较于常规的短期光伏发电功率预测模型,具有更强的鲁棒性和高精度性。展开更多
Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive mode...Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.展开更多
文摘针对光伏发电功率存在随机波动性的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化长短期记忆(long short term memory,LSTM)神经网络的短期光伏发电功率预测方法。首先,通过VMD算法将多维光伏特征数据分解为若干不同频率的本征模态和残差分量,以降低原始序列的非平稳性;然后,采用ISSA对LSTM神经网络超参数进行全局寻优,建立了不同模态序列分量下的ISSA-LSTM组合模型;最后,使用训练好的组合模型对各分解的子序列模态特征分量进行多维预测,并将各层模态预测序列叠加组合成最终的输出结果。仿真结果表明,构建的VMD-ISSA-LSTM组合模型相较于常规的短期光伏发电功率预测模型,具有更强的鲁棒性和高精度性。
基金supported by the National Natural Science Foundation of China(Nos.62172287,62102273)。
文摘Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.
