针对CQG2000精度无法满足实际工作的情况,提出一种新的重力似大地水准面与GPS水准的拟合方法——残差模型法,利用CQG2000、较密集的GPS水准点,采用残差模型法建立吉林省西部地区似大地水准面模型(Jilin west quasi-geoid,JLWQG)。论述建...针对CQG2000精度无法满足实际工作的情况,提出一种新的重力似大地水准面与GPS水准的拟合方法——残差模型法,利用CQG2000、较密集的GPS水准点,采用残差模型法建立吉林省西部地区似大地水准面模型(Jilin west quasi-geoid,JLWQG)。论述建立JLWQG的三角剖分双线性内插算法及其适用性,检测结果表明,JLWQG精度达到了±0.05m,JLWQG在吉林省西部地区基础测绘更新工作中进行了大面积的应用,取得了满意的结果。展开更多
Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 20...Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.展开更多
文摘针对CQG2000精度无法满足实际工作的情况,提出一种新的重力似大地水准面与GPS水准的拟合方法——残差模型法,利用CQG2000、较密集的GPS水准点,采用残差模型法建立吉林省西部地区似大地水准面模型(Jilin west quasi-geoid,JLWQG)。论述建立JLWQG的三角剖分双线性内插算法及其适用性,检测结果表明,JLWQG精度达到了±0.05m,JLWQG在吉林省西部地区基础测绘更新工作中进行了大面积的应用,取得了满意的结果。
基金Soft Science Research Project in Shanxi Province of China(2017041030-5)Science Fund Projects in North University of China(XJJ2016037)
文摘Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.
