基于GA-BP和POS-BP神经网络的光伏电站出力短期预测
当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题。基于本地5 k W小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用BP以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型。实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度。其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测...
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| Published in | 电力系统保护与控制 Vol. 43; no. 20; pp. 83 - 89 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
齐齐哈尔大学通信与电子工程学院,黑龙江 齐齐哈尔,161006%哈尔滨师范大学计算机与信息工程学院,黑龙江 哈尔滨,150080
2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
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| Abstract | 当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题。基于本地5 k W小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用BP以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型。实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度。其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强。 |
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| AbstractList | 当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题。基于本地5 k W小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用BP以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型。实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度。其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强。 TM715; 当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题.基于本地5 kW小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用 BP 以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型.实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度.其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强. |
| Abstract_FL | In the current PV output short-term forecast, BP or optimization BP neural network algorithm is used commonly, which has problems of single optimization algorithm, the lack of a variety of optimization algorithms for comparison and selection, and big forecast error. Therefore, based on local 5 kW small-scale distributed PV power station, considering the related factors that influence PV output such as solar radiation intensity, environmental temperature, wind speed and historical generation data of photovoltaic power station, this paper uses BP, GA-BP and POS-BP neural network algorithm respectively to construct short-term prediction model of PV output in sunny, cloudy and rainy weather conditions. Test results show that three kinds of neural network prediction models all reach certain prediction accuracy under three different weather conditions, among which GA-BP and POS-BP prediction models reduce the prediction errors compared to the traditional BP model, and POS algorithm has a better optimization effect on BP neural network prediction model and a stronger applicability compared to GA algorithm, and further reduces the prediction errors. |
| Author | 姚仲敏 潘飞 沈玉会 吴金秋 于晓红 |
| AuthorAffiliation | 齐齐哈尔大学通信与电子工程学院,黑龙江齐齐哈尔161006 哈尔滨师范大学计算机与信息工程学院,黑龙江哈尔滨150080 |
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| Author_FL | SHEN Yuhui YU Xiaohong WU Jinqiu YAO Zhongmin PAN Fei |
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| Keywords | POS-BP算法 GA-BP算法 光伏发电短期预测 BP神经网络算法 GA-BP algorithm POS-BP algorithm BP neural network algorithm photovoltaic power short-term prediction |
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| Notes | In the current PV output short-term forecast, BP or optimization BP neural network algorithm is used commonly, which has problems of single optimization algorithm, the lack of a variety of optimization algorithms for comparison and selection, and big forecast error. Therefore, based on local 5 kW small-scale distributed PV power station, considering the related factors that influence PV output such as solar radiation intensity, environmental temperature, wind speed and historical generation data of photovoltaic power station, this paper uses BP, GA-BP and POS-BP neural network algorithm respectively to construct short-term prediction model of PV output in sunny, cloudy and rainy weather conditions. Test results show that three kinds of neural network prediction models all reach certain prediction accuracy under three different weather conditions, among which GA-BP and POS-BP prediction models reduce the prediction errors compared to the traditional BP model, and POS algorithm has a better optimization effect |
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| Publisher | 齐齐哈尔大学通信与电子工程学院,黑龙江 齐齐哈尔,161006%哈尔滨师范大学计算机与信息工程学院,黑龙江 哈尔滨,150080 |
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| Snippet | 当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题。基于本地5 k W小型分布式光伏... TM715; 当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题.基于本地5 kW小型分布... |
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| SubjectTerms | BP神经网络算法 GA-BP算法 POS-BP算法 光伏发电短期预测 |
| Title | 基于GA-BP和POS-BP神经网络的光伏电站出力短期预测 |
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