基于双深度神经网络的光功率预测方法

光伏发电是新兴的清洁能源发电方式之一,其光功率受辐照度等环境因素影响较大,导致注入电网的电量不稳定。采集的环境数据能准确预测发电量变化趋势,对电网平稳运行具有重要意义。现有光功率预测方法大多采用单个模型构建预测结构,当面对不同环境数据时预测结果不够稳定。文中提出一种基于双深度神经网络的光功率预测方法,该方法以BPNN(back propagation neural networks)和LSTM(long short term memory)为基础判别器,并通过遗传算法将二者融合为更加精确和鲁棒的光功率预测模型。在东北电网实际数据集上的实验结果表明,相比现有单一神经网络模型,文中提出的方法具有更高的判别精度,且预测结果更加稳定。

Optical power prediction method based on double deep neural networks

Photovoltaic power generation is one of the emerging clean energy power generation methods. However, its efficiency is severely influenced by light intensity in the external environment, resulting in unstable electricity input to the power grid. Therefore, it is very important to predict the trend of change in power generation through collecting and analyzing external environmental factors. Currently, most of the existing methods use a single model to construct the prediction structure, which leads to unstable prediction results when faced with different environmental data. To address this problem, we propose an optical power prediction method based on double deep neural networks. It employs BPNN (back propagation neural networks) and LSTM (long short term memory) as the basic discriminators and combines them into a more accurate and robust optical power prediction model through the genetic algorithm. Experiments on the real datasets of northeast power grid show that compared with existing single neural network models, the proposed method has higher discrimination accuracy and more stable prediction results.