In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
针对现有光功率预测模型在极端天气下预测精度低、天气场景特征解析方式粗糙以及动态补偿机制缺失等问题,提出了一种考虑多场景和差异化补偿策略的光功率预测方法。该方法提出混合天气类型概念,并通过构建具有优化评价功能的聚类(cluste...针对现有光功率预测模型在极端天气下预测精度低、天气场景特征解析方式粗糙以及动态补偿机制缺失等问题,提出了一种考虑多场景和差异化补偿策略的光功率预测方法。该方法提出混合天气类型概念,并通过构建具有优化评价功能的聚类(clustering with optimal evaluation function,COEF)算法,实现天气状态场景的自适应分类;基于极限学习机构建基础值预测模型,并阐明多场景的补偿机理,通过对不同天气场景设计针对性的误差补偿模型,实现对基础预测值的多尺度校正,提高算法的预测精度。最后,选择不同地域和气候特点的多场站实际数据进行仿真测试。仿真结果表明:与物理模型及传统机器学习算法相比,所提出的光功率预测方法在多时间尺度、多场景工况下均有更好的预测效果。展开更多
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
文摘针对现有光功率预测模型在极端天气下预测精度低、天气场景特征解析方式粗糙以及动态补偿机制缺失等问题,提出了一种考虑多场景和差异化补偿策略的光功率预测方法。该方法提出混合天气类型概念,并通过构建具有优化评价功能的聚类(clustering with optimal evaluation function,COEF)算法,实现天气状态场景的自适应分类;基于极限学习机构建基础值预测模型,并阐明多场景的补偿机理,通过对不同天气场景设计针对性的误差补偿模型,实现对基础预测值的多尺度校正,提高算法的预测精度。最后,选择不同地域和气候特点的多场站实际数据进行仿真测试。仿真结果表明:与物理模型及传统机器学习算法相比,所提出的光功率预测方法在多时间尺度、多场景工况下均有更好的预测效果。
