A NEW HYBRID FORECASTING ALGORITHM AND ITS APPLICATION IN ECONOMIC ANALYSIS

There exists a great deal of periodic non-stationary processes in natural,social and eco- nomical phenomenon.It is very important to realize the dynamic analysis and real-time forecast within a period.In this letter,a wavelet-Kalman hybrid estimation and forecasting algorithm based on step-by-step filtering with the real-time and recursion property is put forward.It combines the advantages of Kalman filter and wavelet transform.Utilizing the information provided by multi- sensor effectively,this algorithm can realize not only real-time tracking and dynamic multi-step fore- casting within a period,but also the dynamic forecasting between periods,and it has a great value to the system decision-making.Simulation results show that this algorithm is valuable.

A New Economy Forecasting Method Based on Data Barycentre Forecasting Method

A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China

Surface solar irradiance(SSI)nowcasting(0-3 h)is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants.In this study,an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A(FY-4A)geostationary satellite.The system is composed of two key components:The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model.The second component is the SSI nowcasting model,the core of which is the derivation of the cloud motion vector(CMV)using the block-matching method.The goal of simultaneous estimation and nowcasting of global horizontal irradiance(GHI)and direct normal irradiance(DNI)is fulfilled.The system was evaluated under different sky conditions using SSI measurements at Xianghe,a radiation station in the North China Plain.The results show that the accuracy of GHI estimation is higher than that of DNI estimation,with a normalized root-mean-square error(nRMSE)of 22.4%relative to 45.4%.The nRMSE of forecasting GHI and DNI at 30-180 min ahead varied within 25.1%-30.8%and 48.1%-53.4%,respectively.The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern,being lower in spring-summer and higher in autumn-winter.The FY-4A has great potential in supporting SSI nowcasting,which promotes the development of photovoltaic energy and the reduction of carbon emissions in China.The system can be improved further if calibration of the empirical method is improved.

Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

SURGE FORECAST FOR AERONAUTICAL ENGINES WITH EXTENDED KALMAN ESTIMATION

Abstract In this paper,the theory of extended Kalman estimation is applied to state estimate ofcompression system, for which a nonlinear model is developed by Greitzer.A criterion ofdetermining whether surge will occur in a turbine engine is presented.The combination ofstate estimation and the criterion of determining surge forms a surge prediction algorithm,which is the theoretical basis of designing a surge indicator for the turbine engine.