东北电力技术2026，Vol.47Issue(8)：35-39,43,6.
基于改进SSA优化CNN-LSTM-Attention混合模型的光伏功率预测
Solar Power Prediction Method Based on Improved SSA Optimized CNN-LSTM-Attention Hybrid Model
王琳语1
作者信息
- 1. 辽宁工程技术大学电气与控制工程学院,辽宁 葫芦岛 125000
- 折叠
摘要
Abstract
It affects the output of photovoltaic power by meteorological factors such as irradiance and temperature,showing significant randomness and volatility,which poses difficulties for the high-precision prediction of photovoltaic power.It proposes a CNN-LSTM-Attention hybrid prediction model optimized based on the improved sparrow search algorithm in response to the above problems.This model utilizes a convolutional neural network to extract the local spatial features of the input data,combines a long short-term memory network to capture the long-term temporal dependence rules of the power sequence,and introduces an attention mechanism to dynamically weight the hidden states,thereby enhancing the model's perception ability of features at critical moments.According to the local convergence problem existing in the standard sparrow search algorithm,through integrating Tent chaotic mapping,nonlinear adaptive weights and Levy flight strategies,it automatically optimizes based on the hyperparameters of the hybrid model.The simulation results based on the measured data of a certain photovoltaic power station show that the model can effectively reduce the prediction deviation caused by random meteorological factors,and the prediction output is in high agreement with the actual observed values,which has certain application value in the field of photovoltaic power prediction.关键词
光伏功率预测/麻雀搜索算法/卷积神经网络/长短期记忆/注意力机制Key words
solar power prediction/sparrow search algorithm/convolutional neural network/long short-term memory/attention mechanism分类
信息技术与安全科学引用本文复制引用
王琳语..基于改进SSA优化CNN-LSTM-Attention混合模型的光伏功率预测[J].东北电力技术,2026,47(8):35-39,43,6.
