基于Spiking神经网络的光伏系统发电功率预测

摘要

为了提高光伏系统发电功率预测的精度,本文提出一种基于Spiking神经网络(SNN)的预测模型.该神经网络采用精确脉冲时间的编码方式,更接近真实的生物神经系统,具有强大的计算能力.考虑季节类型、天气类型和大气温度等主要影响因素,该模型采用灰色关联分析法选取相似日.本文应用实际光伏发电系统的数据分别对基于SNN、BP人工神经网络(BP-ANN)和支持向量机(SVM)的预测模型进行测试和评估.预测结果表明:SNN预测模型相比于BP-ANN和SVM模型有较高的预测精度和较强的适用性,可以为光伏系统发电功率预测提供一种可行方法.%A forecasting model based on Spiking neural network (SNN) was proposed in this paper to improve the fore-casting accuracy of power generation from photovoltaic system (PVS) . This neural network uses temporal encoding scheme with precise time of spikes, which is closer to the real biological neural system, thus it has powerful computing capability. Considering the main influencing factors such as season types, weather types and atmospheric temperature, the proposed model uses grey correlation analysis to select similar days. The data from a practical PVS were adopted to test and evaluate the forecasting models based on SNN, back propagation artificial neural network (BP-ANN) and sup-port vector machine (SVM), respectively. The forecasting results reveal that compared with BP-ANN and SVM models, the SNN model has a relatively higher forecasting accuracy and a more robust applicability, which can provide a feasible way to forecast the power generation from PVS.