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Application of Neural Networks in Wind Power
(Generation) Prediction
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这是基于matlab的风功率预测模型及程序，与大家一起分享！-This is a matlab-based wind power prediction models and procedures, and to share with everyone!
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A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal_rep
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利用新陈代谢灰色预测、样本自适应BP 神经网络和时间序列分析分别进行风电功率实时预测和日前预测，并采用熵值取权法确定组合权重，引入自控机制，构建反馈，提出组合预测法和基于时间序列的卡尔曼滤波法。研究结果表明，组合预测模型能减少各预测点较大误差的出现，而卡尔曼滤波能大幅消减原始序列的波动影响。-Use of metabolic gray forecast, sample adaptive BP neural network and time sequence analysis respective
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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
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用BP神经网络和rbf神经网络预测风电功率-BP neural network and rbf neural network forecasting wind power
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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.
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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
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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 network, threshold, carry wind
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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
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运用GA遗传算法优化BP网络，对风电功率进行预测，含实际数据和案例。-GA genetic algorithm to optimize the use of BP network prediction for wind power, with actual data and case studies.
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含NWP数值天气预报和不含NWP数值天气预报的BP神经网络预测风电功率两种方法进行比较，含数据，实际案例。-With and without NWP NWP Numerical Weather Prediction NWP BP neural network prediction of wind power are two methods were compared with the data, the actual case.
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BP神经网络预测风电场功率，利用BP神经网络对风电场未来48小时内的功率进行预测-Wind power prediction for wind farm with BP neural network
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相关向量机用于风电场功率预测和各种大数据分类问题，单变量输出-Relevance vector machine for a wind farm power prediction and a variety of large data classification problem, univariate output
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关于风电场风速及发电功率预测的集中方法研究综述-A summary of the research on wind speed and generation power prediction of wind farm
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此文档为pso优化的BP神经网络预测风电场出力的代码-pso-bp wind power prediction
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使用风电场历史数据，基于pso-bp算法进行风电功率点预测(PSO-BP algorithm for prediction of wind power)
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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 safe, reliable, continuous and s
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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 learning machine combination predicti
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粒子群结合神经网络智能算法优化最值问题。(And the output of the fan is tracked and predicted in real time based on the wind power prediction of the PSO algorithm.)
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