Photometric-network real-time-correction self-learning ARMA model photovoltaic power prediction method

Abstract

The invention discloses a photometric-network real-time-correction self-learning ARMA model photovoltaic power prediction method which includes inputting data and obtaining an ARMA (autoregressive moving average model) parameter, that is, model training; inputting optical resource monitoring system data and operating monitoring system data, and correcting a start-up capacity in a real-time manner according to the operating monitoring data; establishing an ARMA model so as to obtain a photovoltaic-power super-short-term prediction result; introducing real-time photometric-station data to carry out real-time correction on the photovoltaic-power super-short-term prediction result; and carrying out post-evaluation on the real-time corrected prediction result and analyzing an error between a prediction value and an actual measurement value and carrying out the model training again if the prediction error is larger than an allowed maximum error. Through introduction of the real-time photometric-station data, real-time correction is carried out on the photovoltaic generation power super-short-term prediction result so that a defect, which exists in the prior ARMA technologies, that a photovoltaic-generation-power super-short-term prediction precision is low is overcome and an objective of high-precision photovoltaic-generation-power super-short-term prediction is achieved.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power prediction in the process of new energy power generation, in particular to a self-learning ARMA model photovoltaic power ultra-short-term prediction method for real-time correction of photometric networks. Background technique [0002] After my country's photovoltaic power generation enters the stage of large-scale development, most of the large-scale new energy bases 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 through long distances and high voltages. Go to the 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 ...

AI Technical Summary

Problems solved by technology

The ARMA method has many advantages, so it is widely used in the ultra-short-term prediction of photovoltaic power generation, but the biggest disadvantage of ARMA is the hysteresis of its prediction—that is, when the output of photovo

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