摘要
光伏发电功率受自然环境因素影响较大,具有很强的随机性和波动性,准确及时的光伏发电功率预测对电网的调度运行具有重要的意义.提出了一种基于PSO与ELM组合算法的短期光伏发电功率预测模型.该模型通过调整粒子群优化算法(particle swarm optimization, PSO)不同阶段的寻优重点,为极限学习机(extreme learning machine, ELM)设定出了最佳网络参数,避免了ELM随机产生输入层权值和隐含层阈值造成的网络不稳定问题.同时结合传统神经网络和ELM网络隐含层节点选取原则为组合模型,设定了最佳隐含层节点数,提高了模型预测精度.实际算例验证了组合算法模型能够有效提高短期光伏发电功率预测的预测精度.
Influenced by the natural environment factors, photovoltaic power had strong randomness and volatility, therefore an accurate and timely forecast to photovoltaic power was of significances to the power grid dispatching operation.Based on PSO and ELM combined algorithm, a short-term photovoltaic power prediction model was proposed.This model sought advantages and key points at different stages and set the optimizing algorithm (PSO).It also could avoid the problem of network instability caused by the random generation of input layer weight and hidden layer threshold by ELM.In the meantime, combining the most of traditional neural network and ELM network, the optimal hidden layer number was set for the combined model to improve the model prediction accuracy.Through the practical example, it demonstrated that the combined algorithm model could effectively improve the prediction accuracy of short-term photovoltaic power generation prediction.
作者
蒋建东
余沣
董存
常朝辉
陈海刚
JIANG Jiandong;YU Feng;DONG Cun;CHANG Chaohui;CHEN Haigang(Industrial Technology Research Institute, Zhengzhou University, Zhengzhou 450001, China;National Power Dispatch Communication Center,State Grid Corporation of China, Beijing 100031, China;Songxian Power Supply Branch, Henan Electric Power Bureau, Luoyang 471400, China)
出处
《郑州大学学报（理学版）》
CAS
北大核心
2019年第3期120-126,共7页
Journal of Zhengzhou University:Natural Science Edition
基金
国家自然科学基金项目(51507155)
关键词
光伏发电
功率预测
极限学习机
粒子群优化算法
预测精度
photovoltaic power generation
power prediction
extreme learning machine
particle swarm optimization algorithm
prediction accuracy
