Ultra-short-term photovoltaic generation power prediction method based on self-learning composite data source

Abstract

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on a self-learning composite data source. The ultra-short-term photovoltaic generation power prediction method based on the self-learning composite data source comprises the steps that data are input to enable parameters of an autoregression moving average model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression moving average model determined according to the parameters of the autoregression moving average model, so that a prediction result is obtained, post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the model and estimation of the parameters of the model are conducted again if a predicted error is larger than an allowable maximum error. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact the composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of forecasting photovoltaic power generation in the process of new energy power generation, in particular to a super-short-term forecasting method for photovoltaic power generation based on self-learning composite data derived from a regression moving average model. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging pow...

AI Technical Summary

Problems solved by technology

With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of photovoltaic power generation have brought many problems to the safe, stable and economical operation of the power grid.

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