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windspeedsvm1
- 利用支持向量机进行风速的预测，从而被风力发电所利用，挺高风电功率预测的可靠性-Using support vector machines for wind speed prediction, which was exploited by wind power, wind power prediction pricey reliability
artificial-neural-network-method
- 基于人工神经网络法的风电功率预测matlab程序，可预测未来几十个小时内的风电功率。-Based on Artificial Neural Network Method wind power prediction matlab program, you can predict the future of dozens of hours of wind power.
MyBP-lxf
- 基于NWP数据和BP-人工神经网络的超短期风电功率预测-Based on NWP data and BP-ANN ultra short-term wind power prediction
ICA-wind-prediction
- 采用最先进的殖民竞争算法Imperialist competition algorithm优化BP神经网络的初始权值、阈值，进行风电功率预测，带数据和实例，ica为主程序-Using the most advanced colonial competitive algorithm Imperialist competition algorithm to optimize the initial weights of BP neural
PSO-BP-wind-power
- 采用粒子群算法PSO优化BP神经网络，进行风电功率预测，含实际数据和案例-Particle swarm optimization PSO BP neural network for wind power prediction, including the actual data and case
ssvmbaocun
- 支持向量机用于风电功率预测，误差在15 以内。-Support vector machines for wind power prediction
psosvm
- 基于粒子群优化的支持向量机风电功率预测，采用PSO对支持向量机算法进行优化。-Based on PSO support vector machine wind power prediction, using PSO support vector machine algorithm for optimization.
wind-power
- 基于极限学习机的短期风电功率预测,包含了风电功率序列，风电功率曲线拟合-wind power forecast
wind-heat-power-forecast-
- 风热或者风电功率预测软件，内附说明书，良心品质！matlabGUI编写，操作更简单！-wind power forecard
yan-PSO_BP
- PSO优化BP，实现短期风电功率预测,考虑了数据插值前处理和预测结果的再优化.pso optimized BP for wind power short term forecasting-pso optimized BP for wind power short term forecasting
huadong
- 自己编的对于风电功率预测中风速预测采用滑动平均法-Their compiled for wind power prediction of wind speed prediction using the moving average method
her
- 在风电功率预测中用三次HERMIERT对滑动平均法的优化-In wind power prediction by three HERMIERT optimization of moving average method
svm
- 风电功率预测，通过改进的支持向量机模式进行预测，(wind power prediction)
MY DATA正态分布、T分布、高斯分布拟合曲线
- 风电功率预测误差的正态分布，t分布，贝塔分布(Wind power prediction error of the normal distribution, t distribution and beta distribution)
xiaobo
- 小波风电功率预测，具有很高的准确性，跟踪风电功率(Wavelet wind power prediction)
wind power prediction
- 准确的风电功率预测有利于含大规模风电电力系统的安全可靠、持续稳定运行，掌握风电功率预测误差的分布特征，对风电大规模并网有重要意义。以吉林省某风电场的实测数据为例，对风电功率进行超短期预测，利用非参数估计对预测误差分布进行拟合，分析了非参数估计与预测方法、预测时间间隔、预测误差概率分布形态以及风电场装机容量的关系，验证了所提出方法的有效性。(Accurate wind power prediction is conducive to sa
wind power forecasting based on EWT-KELM
- 针对短期风电功率预测，提出一种基于经验小波变换预处理的核极限学习机组合预测方法。首先采用 EWT 对风电场实测风速数据进行自适应分解并提取具有傅立叶紧支撑的模态信号分量，针对每个分量分别构建 KELM 预测模型，最后对各个预测模型的输出进行叠加得到风速预测值并根据风电场风功特性曲线可得对应风电功率预测值。(Aiming at short-term wind power prediction, a kernel-based learnin
pso优化BP
- 使用pso优化神经网络，使风电功率预测达到更高的精度(a pso algorithm used to optmize bp neural network)
LSSVM
- 结合风场实时数据以及风场气象数据，分析了实时数据并制定了数据清洗规则；针对风电功率预测领域预测精度低的问题，采用lssvm算法进行预测。(Combined with real-time wind field data and wind field meteorological data, the real-time data was analyzed and data cleaning rules were formulated. Fo
arma
- 用于风功率预测的ARMA代码，可在matlab上运行，包含风电数据。(ARMA code for wind power prediction can be run on matlab, including wind power data)