Combining multiple trending models for photovoltaics plant output forecasting
AssigneeSiemens Aktiengesellschaft
InventorsChao Yuan, Amit Chakraborty, Eberhard Ritzhaupt-Kleissl, Holger Hackstein
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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 (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).
Claims7
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.
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.
Dependencies are inferred from the claim text.