| 引用本文: | 吴忠强,贾文静,吴昌韩,赵立儒.基于PSO-BSNN的短期风速预测[J].电力系统保护与控制,2015,43(15):36-41.[点击复制] |
| WU Zhongqiang,JIA Wenjing,WU Changhan,ZHAO Liru.Short-term wind speed forecasting based on PSO-BSNN[J].Power System Protection and Control,2015,43(15):36-41[点击复制] |
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| 摘要: |
| 考虑到风的随机性和波动性，提出一种基于粒子群(PSO)优化B样条神经网络(BSNN)的短期风速预测方法。利用相空间重构方法确定BSNN的输入空间向量，BSNN可以灵活地改变对输入空间的划分和对隐层基函数的定义，对任意的网络输入，隐层基函数的输出只有少数非零，使网络输出简单，收敛速度快。但在传统的BSNN中，对输入空间节点位置的均匀划分是粗糙的，预测结果容易陷入局部极小而影响预测精度。粒子群优化算法是一种智能搜索方法，它具有较强的搜索能力并且容易实现，利用PSO优化BSNN输入空间的节点位置划分，可避免BSNN陷入局部极小并提高预测精度。仿真结果表明，基于PSO-BSNN的预测模型比传统的BSNN和BPNN预测模型具有更高的预测精度。 |
| 关键词: PSO BSNN 相空间重构 短期风速预测 预测模型 |
| DOI：10.7667/j.issn.1674-3415.2015.15.006 |
| 投稿时间：2014-10-31修订日期：2015-01-31 |
| 基金项目:河北省自然科学基金项目(F2012203088) |
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| Short-term wind speed forecasting based on PSO-BSNN |
| WU Zhongqiang,JIA Wenjing,WU Changhan,ZHAO Liru |
| (School of Electric Engineering, Yanshan University, Qinhuangdao 066004, China) |
| Abstract: |
| Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm (PSO-BSNN) is proposed to predict the short-term wind speed. The input space variable of BSNN can be determined by phase space reconstruction, BSNN can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple and the convergence speed is fast, but the traditional method is rough when it divides the input space evenly, and easy to fall into local minimum which will influence the final prediction accuracy. PSO is an intelligent search method, it has strong global search ability and is easy to be realized, which can be used to optimize the BSNN input space internal nodes and it can avoid the prediction result falling into local minimum. Simulation results show that PSO-BSNN has higher prediction accuracy than BSNN and BPNN. |
| Key words: PSO BSNN phase space reconstruction short-term wind speed forecasting prediction model |
