公开(公告)号：US20170336534A1

公开(公告)日：2017-11-23

申请号：US15600020

申请日：2017-05-19

Applicant: THE CATHOLIC UNIVERSITY OF AMERICA

Inventor： Kevin F. Forbes ,  Ernest M. Zampelli

IPC: G01W1/10 ,  G06F17/30 ,  G06F15/18 ,  G06F17/10 ,  G01W1/00

CPC classification number: G01W1/10 ,  F05B2260/8211 ,  G01W2001/006 ,  G01W2201/00 ,  G01W2203/00 ,  G06F16/23 ,  G06F17/10 ,  G06F17/18 ,  G06N20/00 ,  G06Q10/04 ,  G06Q50/06

Abstract: A computer system and method for improving the accuracy of predictions of the amount of renewable energy, such as solar energy and wind energy, available to an electric utility, and/or refine such predictions, by providing improved integration of meteorological forecasts. Coefficient values are calculated for a renewable energy generation model by performing a regression analysis with the forecasted level of renewable energy posted by the utility, forecasted weather conditions and measures of seasonality as explanatory variables. Accuracy is further enhanced through the inclusion of a large number of time series variables that reflect the systematic nature of the energy/weather system. The model also uses the original forecast posted by the system operator as well as variables to control for season.

公开(公告)号：US10656306B2

公开(公告)日：2020-05-19

申请号：US15600020

申请日：2017-05-19

Applicant: THE CATHOLIC UNIVERSITY OF AMERICA

Inventor： Kevin F. Forbes ,  Ernest M. Zampelli

IPC: G01W1/10 ,  G06N20/00 ,  G06F16/23 ,  G06F17/18 ,  G06Q10/04 ,  G06Q50/06 ,  G06F17/10 ,  G01W1/00

Abstract: A computer system and method for improving the accuracy of predictions of the amount of renewable energy, such as solar energy and wind energy, available to an electric utility, and/or refine such predictions, by providing improved integration of meteorological forecasts. Coefficient values are calculated for a renewable energy generation model by performing a regression analysis with the forecasted level of renewable energy posted by the utility, forecasted weather conditions and measures of seasonality as explanatory variables. Accuracy is further enhanced through the inclusion of a large number of time series variables that reflect the systematic nature of the energy/weather system. The model also uses the original forecast posted by the system operator as well as variables to control for season.