电力系统及其自动化学报2017，Vol.29Issue(6)：7-12,44,7.DOI:10.3969/j.issn.1003-8930.2017.06.002
基于Spiking神经网络的光伏系统发电功率预测
Power Generation Forecasting for Photovoltaic System Based on Spiking Neural Network
摘要
Abstract
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.关键词
光伏系统/Spiking神经网络/SpikeProp算法/相似日选择算法/发电功率预测Key words
photovoltaic system(PVS)/Spiking neural network(SNN)/SpikeProp algorithm/similar day selection algo-rithm/power generation forecasting分类
信息技术与安全科学引用本文复制引用
陈通,孙国强,卫志农,李慧杰,CHEUNGKWOKW,孙永辉..基于Spiking神经网络的光伏系统发电功率预测[J].电力系统及其自动化学报,2017,29(6):7-12,44,7.基金项目
国家自然科学基金资助项目(51277052,51107032,61104045) （51277052,51107032,61104045）
