Combining multiple trending models for photovoltaics plant output forecasting

Patent metadata

Field	Value
Publication number	US-10540422-B2
Application number	US-201415301481-A
Country	US
Kind code	B2
Filing date	Apr 14, 2014
Priority date	Apr 4, 2014
Publication date	Jan 21, 2020
Grant date	Jan 21, 2020

Abstract

A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S 301 ); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S 302 ); determining a trending model from the computed features and the forecasted value (S 303 ); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S 304 ).

First claim

What is claimed is:

1. A process for forecasting power generation by a solar power plant using combined trending models, the process comprising:
forecasting an exogenous vector of weather-related exogenous variables for a future time (h) that is likely to affect the ability of the solar power plant to produce electricity;
generating a plurality of trending models representative of different trending ranges of prior data observations, each trending model generation comprising:
extracting a feature from a trending function, wherein the extracted feature represents a power value at the future time (h) based on the trending function; wherein the trending function is associated with a time period (w) of a duration different than other trending functions and derived from prior observed amounts of power generated by the solar power plant during a corresponding time period (w);

predicting an amount of power that will be generated by the solar power plant at the future time (h) from a combination of the trending models, wherein the combination is selectively a linear combination or a nonlinear combination; wherein
on a condition that the linear combination is selected, combining the feature and the forecasted exogenous vector as a sum of:
the forecasted exogenous vector multiplied by a first weighting coefficient vector, and
a sum of the feature multiplied by a respective feature weighting coefficient, wherein the first weighting coefficient and the respective feature weighting coefficient are adjusted according to the trending range; and

on a condition that the non-linear combination is selected, combining the feature and the forecasted exogenous vector using a Gaussian process that takes the feature and the forecasted exogenous vector as inputs, the Gaussian process having automatic relevance determination such that all the inputs are assigned a weight coefficient nonlinearly automatically via a length scale parameter of the Gaussian process.

2. The method of claim 1 , wherein the data variable indicates at least one of: an amount of cloud cover, an ambient temperature, and an amount of humidity.

3. The method of claim 1 , wherein the extracting of the feature comprises:
selecting part of history data that occurs within the duration associated with the feature;
determining a polynomial that best fits the selected part; and
deriving the feature from the determined polynomial and the future time (h).

4. The method of claim 3 , wherein a highest order power of the polynomial is one.

5. A computer program product for forecasting power generation by a solar power plant using combined trending models, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to perform a method comprising:
forecasting an exogenous vector of weather-related exogenous variables for a future time (h) that is likely to affect the ability of the solar power plant to produce electricity;
generating a plurality of trending models representative of different trending ranges of prior data observations, each trending model generation comprising:
extracting a feature from a trending function, wherein the extracted feature represents a power value at the future time (h) based on the trending function; wherein the trending function is associated with a time period (w) of a duration different than other trending functions and derived from prior observed amounts of power generated by the solar power plant during a corresponding time period (w);

predicting an amount of power that will be generated by the solar power plant at the future time (h) from a combination of the trending models, wherein the combination is selectively a linear combination or a nonlinear combination; wherein
on a condition that the linear combination is selected, combining the feature and the forecasted exogenous vector as a sum of:
the forecasted exogenous vector multiplied by a first weighting coefficient vector, and
a sum of the feature multiplied by a respective feature weighting coefficient, wherein the first weighting coefficient and the respective feature weighting coefficient are adjusted according to the trending ranges; and
on a condition that the non-linear combination is selected, combining the feature and the forecasted exogenous vector using a Gaussian process that takes the feature and the forecasted exogenous vector as inputs, the Gaussian process having automatic relevance determination such that all the inputs are assigned a weight coefficient nonlinearly automatically via a length scale parameter of the Gaussian process.

6. The computer product of claim 5 , wherein the computing of the feature comprises:
selecting part of history data that occurs within the duration associated with the feature;
determining a polynomial that best fits the selected part; and
deriving the feature from the determined polynomial and the future time (h).

7. The computer product of claim 6 , wherein a highest order power of the polynomial is one.

Assignees

• Siemens Ag

Inventors

• Yuan Chao
• Chakraborty Amit
• Ritzhaupt-Kleissl Eberhard
• Hackstein Holger

Classifications

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F17/18 Primary

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title