Photovoltaic Power Prediction Model Based on Parallel Neural Network and Genetic Algorithms-猫眼课题宝

DOI：

10.1007/978-981-10-3996-6_8

发表时间：

2016-09

期刊：

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中科院分区：

其他

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作者：

Gaowei Xu;Min Liu

随着大规模光伏发电系统的广泛应用，光伏发电功率预测可以减少光伏发电系统输出功率的不确定性和随机性给系统带来的负面影响。提出了一种基于并行反向传播神经网络（BPNN）和遗传算法的光伏发电功率预测模型，以历史发电功率、历史气象数据和目标日气象数据为输入参数，对光伏发电输出功率进行预测。提出了一种基于MapReduce的并行BPNN算法，通过学习大量训练样本数据，建立输入输出之间的映射关系。此外，提出了一种基于MapReduce的并行遗传算法来优化BPNN的初始权值和阈值。实验结果表明，与传统的光伏发电功率预测模型相比，采用并行BP神经网络和遗传算法的预测模型能显著提高预测精度和速度。

With the wide application of large-scale photovoltaic systems, photovoltaic power prediction can reduce the negative effects caused by the intermittency and randomness of output power for photovoltaic system. This paper proposes a novel photovoltaic power prediction model based on parallel back propagation neural network (BPNN) and genetic algorithms to predict output power, whose input parameters are historical power output data, historical meteorology data, and meteorology data of the objective day. A parallel BPNN algorithm based on MapReduce is proposed to establish a mapping relationship between input and output through studying large amounts of training sample data. Furthermore, a parallel genetic algorithm based on MapReduce is proposed to optimize BPNN initial weights and thresholds. Experiment results show that the proposed model with parallel BPNN and genetic algorithms can significantly improve prediction accuracy and speed, compared with traditional photovoltaic power prediction model.

Photovoltaic Power Prediction Model Based on Parallel Neural Network and Genetic Algorithms

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