Combined Forecast for Wind Power Short-Term Load Based on Gray Neural Network Trained by Particle Swarm Optimization

机译：粒子群优化训练的灰色神经网络风电短期负荷组合预测

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The accurate wind power load forecasting has important significance for power production, power network safe operation and national economy. By comprehensively analyzes the advantages and disadvantages of various forecasting method, combining grey forecast and neural network training by particle swarm optimization, this paper establishes combined forecast model based on gray neural network trained by particle swarm optimization and applies it into short-term load forecasting of wind power. Empirical analysis shows that this method is science and practical.

机译：准确的风电负荷预测对电力生产，电网安全运行和国民经济具有重要意义。通过综合分析各种预测方法的优缺点，结合灰色预测和粒子群优化的神经网络训练，建立了基于灰色神经网络的粒子群优化组合的预测模型，并将其应用于短期负荷预测中。风力。实证分析表明，该方法是科学实用的。

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来源
《CSISE 2011;Conference on computer science, intelligent system and environment》|2012年|p.383-387|共5页
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DongXiao Niu; YaNan Wei; HuanHuan Qiao; Fang Fang;
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中图分类计算技术、计算机技术;
关键词
grey forecasting; particle swarm optimization; neural networks; wind power; load forecasting;

机译：灰色预测粒子群优化;神经网络;风力;负荷预测;

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机译：基于混沌量子行为的粒子群优化神经网络的短期负荷预测

Combined Forecast for Wind Power Short-Term Load Based on Gray Neural Network Trained by Particle Swarm Optimization

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