基于Attention-LSTM的光伏超短期功率预测模型

超短期光伏发电功率预测有利于电网的调度管理,提高电力系统运行效率及经济性.针对传统长短时记忆(LSTM)神经网络在处理长序列输入时易忽略重要时序信息的缺陷,文章提出了一种结合注意力机制(Attention)与LSTM网络的功率预测模型.采用皮尔森相关系数法(Pearson)分析了实验的历史数据集,剔除无关变量,对数据集...

Photovoltaic ultra short-term power forecasting model based on Attention-LSTM

Ultra-short-term photovoltaic power generation prediction is beneficial to power grid dispatching management, improve power system operation efficiency and economy. In view of the defect of traditional long and short time memory (LSTM) neural network that tends to ignore important timing information when processing long sequence input, this paper proposes a power prediction model combining Attention mechanism and LSTM network. In this paper, the Pearson correlation coefficient method is firstly used to analyze the historical data set of the experiment. Irrelevant variables are eliminated and dimensionality reduction is carried out on the data set to simplify the structure of the prediction model. On this basis, Attention mechanism and LSTM network are combined as prediction models. The Attention mechanism assigns different weights to the input characteristics of LSTM, which makes the prediction model more effective in processing input of long time series. The model proposed in this paper is trained and compared with the measured data of a photovoltaic power station. The prediction model proposed in this paper can make full use of historical data, be more sensitive to the key information in the input sequence of a long time, and have higher prediction accuracy.