The medium-short term forecast for a certain kinds of main earthquake events might be possible with the time-to-failure method presented by Varnes (1989), Bufe and Varnes (1993), which is to simulate an accelerative r...The medium-short term forecast for a certain kinds of main earthquake events might be possible with the time-to-failure method presented by Varnes (1989), Bufe and Varnes (1993), which is to simulate an accelerative releasing model of precursory earthquake energy. By fitting the observed data with the theoretical formula, a medium-short term forecast technique for the main shock events could be established, by which the location, time and magnitude of the main shock could be determined. The data used in the paper are obtained from the earthquake catalogue recorded by Yunnan Regional Seismological Network with a time coverage of 1965~2002. The statistical analyses for the past 37 years show that the data of M2.5 earthquakes were fairly complete. In the present paper, 30 main shocks occurred in Yunnan region were simulated. For 25 of them, the forecasting time and magnitude from the simulation of precursory sequence are very close to the actual values with the precision of about 0.57 (magnitude unit). Suppose that the last event of the precursory sequence is known, then the time error for the forecasting main shock is about 0.64 year. For the other 5 main shocks, the simulation cannot be made due to the insufficient precursory events for the full determination of energy accelerating curve or disturbance to the energy-release curve. The results in the paper indicate that there is no obviously linear relation in the optimal searching radius for the main shock and the precursory events because Yunnan is an active region with damage earthquakes and moderate and small earthquakes. However, there is a strong correlation between the main shock moment and the coefficient k/m. The optimal fitting range for the forecasting time and magnitude can be further reduced using the relation between the main shock moment lgM0 and the coefficient lgk/m and the value range of the restricting index m, by which the forecast precision of the simulated main shock can be improved. The time-to-failure method is used to fit 30 main shocks in the paper and more than 80% of them have acquired better results, indicating that the method is prospective for its ability to forecast the known main shock sequence. Therefore, the prospect is cheerful to make medium-short term forecast for the forthcoming main shocks by the precursory events.展开更多
针对电力负荷数据中存在的不平衡小类样本导致负荷预测精度不高问题,提出一种基于K-means-SyMProD-PCA数据预处理及NPMA-LSSVM模型的电力负荷预测方法。通过改进的K均值(K-means)方法根据电力负荷特性对其进行预分类,并构建分类标签作...针对电力负荷数据中存在的不平衡小类样本导致负荷预测精度不高问题,提出一种基于K-means-SyMProD-PCA数据预处理及NPMA-LSSVM模型的电力负荷预测方法。通过改进的K均值(K-means)方法根据电力负荷特性对其进行预分类,并构建分类标签作为输入特征;针对电力负荷分类后的样本类别不平衡问题,采用基于概率分布合成小类样本(synthetic minority based on probabilistic distribution,SyMProD)方法扩充小类样本数据以平衡样本类别;为了消除具有重复信息的特征,基于主成分分析(principal component analysis,PCA)方法提取电力负荷主要特征;最后建立最小二乘支持向量机(least square support vector machine,LSSVM)电力负荷预测模型,采用非线性惯性因子和多项式变异的蜉蝣算法对模型参数进行优化,以提高负荷预测精度。分别采用第9届电工杯建模大赛数据和扬中市2015年1443家企业的用电量数据作为验证数据,结果表明,结合K-means-SyMProD-PCA负荷数据预处理,NPMA-LSSVM预测模型有效降低了电力负荷预测误差,能够较好地解决不平衡小类样本情况下的中短期电力负荷预测问题,具有一定的适用性。展开更多
文摘The medium-short term forecast for a certain kinds of main earthquake events might be possible with the time-to-failure method presented by Varnes (1989), Bufe and Varnes (1993), which is to simulate an accelerative releasing model of precursory earthquake energy. By fitting the observed data with the theoretical formula, a medium-short term forecast technique for the main shock events could be established, by which the location, time and magnitude of the main shock could be determined. The data used in the paper are obtained from the earthquake catalogue recorded by Yunnan Regional Seismological Network with a time coverage of 1965~2002. The statistical analyses for the past 37 years show that the data of M2.5 earthquakes were fairly complete. In the present paper, 30 main shocks occurred in Yunnan region were simulated. For 25 of them, the forecasting time and magnitude from the simulation of precursory sequence are very close to the actual values with the precision of about 0.57 (magnitude unit). Suppose that the last event of the precursory sequence is known, then the time error for the forecasting main shock is about 0.64 year. For the other 5 main shocks, the simulation cannot be made due to the insufficient precursory events for the full determination of energy accelerating curve or disturbance to the energy-release curve. The results in the paper indicate that there is no obviously linear relation in the optimal searching radius for the main shock and the precursory events because Yunnan is an active region with damage earthquakes and moderate and small earthquakes. However, there is a strong correlation between the main shock moment and the coefficient k/m. The optimal fitting range for the forecasting time and magnitude can be further reduced using the relation between the main shock moment lgM0 and the coefficient lgk/m and the value range of the restricting index m, by which the forecast precision of the simulated main shock can be improved. The time-to-failure method is used to fit 30 main shocks in the paper and more than 80% of them have acquired better results, indicating that the method is prospective for its ability to forecast the known main shock sequence. Therefore, the prospect is cheerful to make medium-short term forecast for the forthcoming main shocks by the precursory events.
文摘针对电力负荷数据中存在的不平衡小类样本导致负荷预测精度不高问题,提出一种基于K-means-SyMProD-PCA数据预处理及NPMA-LSSVM模型的电力负荷预测方法。通过改进的K均值(K-means)方法根据电力负荷特性对其进行预分类,并构建分类标签作为输入特征;针对电力负荷分类后的样本类别不平衡问题,采用基于概率分布合成小类样本(synthetic minority based on probabilistic distribution,SyMProD)方法扩充小类样本数据以平衡样本类别;为了消除具有重复信息的特征,基于主成分分析(principal component analysis,PCA)方法提取电力负荷主要特征;最后建立最小二乘支持向量机(least square support vector machine,LSSVM)电力负荷预测模型,采用非线性惯性因子和多项式变异的蜉蝣算法对模型参数进行优化,以提高负荷预测精度。分别采用第9届电工杯建模大赛数据和扬中市2015年1443家企业的用电量数据作为验证数据,结果表明,结合K-means-SyMProD-PCA负荷数据预处理,NPMA-LSSVM预测模型有效降低了电力负荷预测误差,能够较好地解决不平衡小类样本情况下的中短期电力负荷预测问题,具有一定的适用性。
