A photovoltaic ultra-short-term combination prediction method based on an Adaboost algorithm

Abstract

The invention provides a photovoltaic ultra-short-term combined prediction method based on an Adaboost algorithm, and belongs to the field of photovoltaic power calculation, and the method comprises the steps: obtaining the prediction accuracy of each sub prediction model for each sample point, and calculating the weight of each sub prediction model in a combined model according to an error rate;Updating the weight distribution of the training samples; According to the updated sample point weight, the error rate of each sub-prediction model is recalculated, iteration is ended when a preset iteration termination condition is met, and a final combined prediction model including the sub-prediction models is obtained; And according to the finally obtained combined prediction model. Differentweak learners are trained for the same training set, and then the weak learners are integrated to form a stronger combined prediction model. By introducing a prediction standard reaching rate concept,taking prediction standard reaching as a target and combining an Adaboost algorithm idea, combined optimization of a plurality of weak learning algorithms is realized, prediction risks of a single prediction method can be avoided, prediction precision is improved, and stability of a prediction model is enhanced.

Description

technical field [0001] The present application belongs to the field of photovoltaic power calculation, and in particular relates to a photovoltaic ultra-short-term combined prediction method based on the Adaboost algorithm. Background technique [0002] In recent years, the photovoltaic power forecasting technology has gradually developed and matured, and the photovoltaic power forecasting system has also been applied in practice, playing an important role in formulating day-ahead power generation plans, promoting new energy consumption, and ensuring the economic and safe operation of power grids. Photovoltaic power forecasting can be divided into ultra-short-term, short-term, medium- and long-term forecasts according to the time scale. In the field of ultra-short-term photovoltaic power forecasting, various types of forecasting methods have been formed, mainly including physical forecasting methods based on cloud image processing, forecasting methods based on NWP or real-ti...

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Problems solved by technology

No matter which prediction method is used, there is a risk of prediction failure. How to improve the stability of the prediction model is an important issue that affects the application effect of the photovoltaic power prediction system.

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