基于混合神经网络的光伏电量预测模型的研究

精确的光伏发电量预测对光伏发电系统的安全运行有重要的作用。然而,由于太阳能的不稳定性、间歇性和随机性,现有光伏发电量的短期预测模型存在预测误差大、泛化能力低等问题。因此,提出一种混合神经网络和注意力机制的分布式光伏电站电量短期预测模型(A-HNN)。利用残差长短期记忆网络与扩展因果卷积相结合提取数据的时间和空间特征,加入注意力机制增强特征选择,给出一种改进的混合神经网络模型。根据发电量数据时间序列本身的特性,选取以日为周期的时间序列数据。最后,通过实验与近期其他模型对比,结果表明在同等条件下此混合模型可以大幅提高光伏发电量预测的精度。

Research on photovoltaic power forecasting model based on hybrid neural network

Accurate photovoltaic power generation prediction plays an important role in the safe operation of photovoltaic power generation system. However, due to the instability, intermittent and randomness of solar energy, the existing short-term prediction models of photovoltaic power generation have problems of large prediction error and low generalization ability. Therefore, a Hybrid Neural Network (A-HNN) and attention mechanism for short-term forecasting of distributed photovoltaic power station is proposed. The temporal and spatial characteristics of data are extracted by Residual LSTM and dilated causal convolution, and an improved hybrid neural network model is obtained by adding attention mechanism to enhance feature selection. According to the characteristics of the time series of power generation data, the time series data with daily cycle are selected. Finally, compared with other recent models, the results show that the hybrid model can greatly improve the accuracy of photovoltaic power generation prediction under the same conditions. This work is supported by Ningxia Natural Science Foundation (No. 2020AAC03034) and Scientific Research Innovation Project of China Western First-class Universities (No. ZKZD2017005).