安全、健康和环境2024，Vol.24Issue(6)：14-19,6.DOI:10.3969/j.issn.1672-7932.2024.06.003
基于CNN-LSTM组合模型光伏预测和负荷预测算法的研究与应用
Research and Application of PV Forecasting and Load Forecasting Algorithm Based on CNN-LSTM Combination Model
邹兵1
作者信息
- 1. 中石化胜利油田分公司生产运行管理中心,山东东营 257000
- 折叠
摘要
Abstract
The installed photovoltaic capacity in the power grid of oilfield enterprises is growing rapidly,but due to the influence of solar irradiance and other meteorological factors,the intermittency and volatility of photovoltaic power generation have a serious impact on the safe and stable operation of the power grid of oilfield enterprises.At the same time,the previous single model prediction of photovoltaic power genera-tion has certain limitations.Combining the features of convolutional neural network(CNN)and long short-term memory network(LSTM),a combined predic-tion model based on CNN-LSTM was proposed,and simulation experiments were carried out in a photovol-taic power station.The experimental results showed that the root-mean-square error and average absolute error of CNN-LSTM model for PV power prediction were 0.212 1 and 0.129 0,respectively,and the root-mean-square error and average absolute error of load prediction were 0.209 7 and 0.115 5,respec-tively.The validity of the model for power and load prediction of photovoltaic power generation was veri-fied.The predicted results can guide the operation plan of the source network load,improve the photo-voltaic consumption,improve the safe and reliable operation level of the power system,and provide ac-curate decision-making support for the safe and low-carbon operation of the oilfield power grid.关键词
光伏发电/功率/负荷预测/模型/安全Key words
photovoltaic power generation/power/load forecasting/model/secure分类
信息技术与安全科学引用本文复制引用
邹兵..基于CNN-LSTM组合模型光伏预测和负荷预测算法的研究与应用[J].安全、健康和环境,2024,24(6):14-19,6.
