| 优化系数的NGM(1,1,k)模型在中长期电量预测中的应用 |
| |
| 引用本文： | 鲁宝春,赵深,田盈,杨杨,李宝国,陈晓英,孙丽颖.优化系数的NGM(1,1,k)模型在中长期电量预测中的应用[J].电力系统保护与控制,2015,43(12):98-103. |
| |
| 作者姓名： | 鲁宝春 赵深 田盈 杨杨 李宝国 陈晓英 孙丽颖 |
| |
| 作者单位： | 辽宁工业大学电气工程学院，辽宁 锦州 121001;国网温州供电公司，浙江 温州 325000;许继集团研发中心，河南 许昌 461000;辽宁工业大学电气工程学院，辽宁 锦州 121001;辽宁工业大学电气工程学院，辽宁 锦州 121001;辽宁工业大学电气工程学院，辽宁 锦州 121001;辽宁工业大学电气工程学院，辽宁 锦州 121001 |
| |
| 基金项目： | 国家自然科学基金项目(61104070) |
| |
| 摘 要： | NGM(1,1,k)灰色模型预测电力系统中长期电量的精度较低,考虑增加一修正量对模型系数进行修正。按照拟合值与真实值的误差平方和最小来求解此修正量,并结合缓冲算子对原始电量数据进行处理,建立了优化系数的灰色电量预测模型。将改进灰色模型应用于两个地区的中长期电量预测中,预测结果表明,优化的灰色模型有效地提高了预测精度。
|
| 关 键 词： | 初值 NGM(1 1 k)灰色模型 中长期电量预测 修正系数 缓冲算子 |
| 收稿时间： | 9/6/2014 12:00:00 AM |
| 修稿时间： | 2015/4/23 0:00:00 |
Mid-long term electricity consumption forecasting based on improved
NGM (1,1,k) gray model |
| |
| LU Baochun,ZHAO Shen,TIAN Ying,YANG Yang,LI Baoguo,CHEN Xiaoying and SUN Liying.Mid-long term electricity consumption forecasting based on improved
NGM (1,1,k) gray model[J].Power System Protection and Control,2015,43(12):98-103. |
| |
| Authors: | LU Baochun ZHAO Shen TIAN Ying YANG Yang LI Baoguo CHEN Xiaoying and SUN Liying |
| |
| Affiliation: | College of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China;State Grid Zhejiang Wenzhou Electric Power Company, Wenzhou 325000, China;XJ Group Research and Development Center, Xuchang 461000, China;College of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China;College of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China;College of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China;College of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China |
| |
| Abstract: | It has lower accuracy in predicting mid-long term electricity consumption of power system by using NGM (1,1, k) gray model. For that a method of adding a correction value to revise the model coefficients is proposed. The least sum of square error between the fitting values and the actual values is used to solve the correction value, the raw data is processed by using the buffer operator, and the improved gray electricity consumption forecasting model is established. The improved model is used to forecast the mid-long term electricity consumption in two regions and the forecasting results show that it effectively enhances the prediction accuracy. |
| |
| Keywords: | initial value NGM (1 1 k) gray model mid-long term electricity consumption forecasting correction factor buffer operator |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《电力系统保护与控制》浏览原始摘要信息 |
|
点击此处可从《电力系统保护与控制》下载全文 |
|
