计算机应用与软件2025，Vol.42Issue(6)：119-126,185,9.DOI:10.3969/j.issn.1000-386x.2025.06.016
基于跳跃连接策略和TCN-BiLSTM的光伏功率预测
PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON SKIP CONNECTION STRATEGY COMBINED WITH TCN-BILSTM
摘要
Abstract
Aimed at the problems of short-term nonlinearity of data and insufficient long-term periodicity capture in the process of photovoltaic power prediction,a photovoltaic power prediction method based on skip connection strategy and TCN-BiLSTM model is proposed.Similar samples were found through the grey correlation degree to form the prediction data set of the day.Temporal convolutional network(TCN)was used to extract local features to maintain the time series of features.The bi-directional long short-term memory(BiLSTM)network using the skip connection strategy was used to learn the long-term and short-term sequence features and combine the attention mechanism to adaptively focus on more important historical states.The experimental results based on actual power station data show that this method can effectively predict photovoltaic power generation,and has smaller prediction error and higher robustness than the general model.关键词
跳跃连接/时间卷积网络/长短期记忆/注意力机制/光伏功率预测Key words
Skip connection/TCN/LSTM/Attention mechanism/PV power forecast分类
信息技术与安全科学引用本文复制引用
王俊,欧阳福莲,周杭霞..基于跳跃连接策略和TCN-BiLSTM的光伏功率预测[J].计算机应用与软件,2025,42(6):119-126,185,9.基金项目
浙江省公益技术应用研究项目(LGG22E070003). （LGG22E070003）
