Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine ...Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.展开更多
为提高光伏电站短期功率预测的精度,提出一种基于经验小波变换(empirical wavelet transform,EWT))和核最小最大概率回归机(kernel mini max probability machine regression,KMPMR)的组合预测模型,对晴天、阴天和雨天3种天气类型下的...为提高光伏电站短期功率预测的精度,提出一种基于经验小波变换(empirical wavelet transform,EWT))和核最小最大概率回归机(kernel mini max probability machine regression,KMPMR)的组合预测模型,对晴天、阴天和雨天3种天气类型下的光伏电站出力分别进行了预测分析。该文首先采用EWT将相似日光伏功率序列分解为具有特征差异的AM-FM分量,然后根据各AM-FM分量的变化特点建立相应的KMPMR预测模型分别进行预测并叠加得到最终预测结果。试验结果表明,相比SVM方法,该文方法在晴天、阴天和雨天可提高预测精度(MAE)分别为56.19%、54.15%和76.33%;相比EMD-KMPMR方法,在降低近一半左右计算规模的同时,可提高预测精度(MAE)分别为9.42%、38.74%和64.52%。以阿克苏地区光伏电站实际运行数据进行试验验证表明,该文方法在3种天气类型下均可取得较高的预测精度。展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No 60602034） and the Natural Science Foundation of Jiangxi Province, China （Grant No 0611031）.
文摘Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.
文摘为提高光伏电站短期功率预测的精度,提出一种基于经验小波变换(empirical wavelet transform,EWT))和核最小最大概率回归机(kernel mini max probability machine regression,KMPMR)的组合预测模型,对晴天、阴天和雨天3种天气类型下的光伏电站出力分别进行了预测分析。该文首先采用EWT将相似日光伏功率序列分解为具有特征差异的AM-FM分量,然后根据各AM-FM分量的变化特点建立相应的KMPMR预测模型分别进行预测并叠加得到最终预测结果。试验结果表明,相比SVM方法,该文方法在晴天、阴天和雨天可提高预测精度(MAE)分别为56.19%、54.15%和76.33%;相比EMD-KMPMR方法,在降低近一半左右计算规模的同时,可提高预测精度(MAE)分别为9.42%、38.74%和64.52%。以阿克苏地区光伏电站实际运行数据进行试验验证表明,该文方法在3种天气类型下均可取得较高的预测精度。
