Cluster photovoltaic power probability prediction method and system, medium and electronic equipment

Abstract

The invention provides a cluster photovoltaic power probability prediction method and system, a medium and electronic equipment, and the method comprises the steps: collecting the historical data of all photovoltaic stations, and carrying out the normalization of the collected historical data; respectively extracting representative features from the input data of the single photovoltaic stations by using an improved convolutional neural network-quantile regression model, and comprehensively extracting correlation features between the regional photovoltaic stations; the improved convolutional neural network-quantile regression model outputting a quantile prediction result of the regional photovoltaic power generation power according to the extracted correlation characteristics between the regional photovoltaic stations. According to the invention, the structure of the convolutional neural network is improved; the improved convolutional neural network firstly carries out feature extraction on each photovoltaic field station in the region, and then carries out correlation feature extraction on the photovoltaic field stations in the whole region, so that the precision of cluster photovoltaic power probability prediction is greatly improved, and the calculation cost is reduced.

Description

technical field [0001] The present disclosure relates to the field of photovoltaic power prediction, and in particular to a method, system, medium and electronic equipment for probabilistic prediction of cluster photovoltaic power. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] The large-scale integration of photovoltaic field stations will bring great challenges to power system operators. Compared with the power prediction of a single photovoltaic station, the power prediction errors of multiple photovoltaic stations can cancel each other out, so the accuracy of cluster photovoltaic power prediction is relatively high, which is called the smoothing effect. At present, some researchers focus on how to predict the photovoltaic power generation in an area in order to provide necessary information for power system operators to make reason...

AI Technical Summary

Problems solved by technology

However, the selection of representative photovoltaic sites can easily affect the prediction accuracy of the statistical upscaling method

If the selection of representative photovoltaic stations is not representative or accurate, the prediction accuracy of the power generation of the final cluster photovoltaic stations will be greatly reduced

[0004] The inventors of the present disclosure found in the research that according to the form of the prediction results, the power prediction methods of photovoltaic stations can be divided into point prediction and probability prediction. For photovoltaic power prediction results, due to the variability and intermittent nature of photovoltaic power generation, point prediction is difficult to accurately describe the power generation of photovoltaic power stations; probabilistic prediction can not only provide power prediction results, but also provide uncertainties corresponding to photovoltaic power prediction results information

The output form of the probability prediction model can be roughly divided into three types: probability density function (PDF) or cumulative distribution function (CDF), confidence interval and quantile, and the existing research mainly focuses on the probability prediction of a single photovoltaic site At present, there are few studies on the probability prediction of cluster photovoltaic power, and it is still unable to provide more accurate and comprehensive regional photovoltaic power probability prediction information.

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