Distributed photovoltaic ultra-short-term forecasting method based on Adaboost clustering and Markov chain

A Markov chain and ultra-short-term prediction technology, applied in character and pattern recognition, data processing applications, instruments, etc., can solve the problems of reduced prediction accuracy and insufficient mining of historical solar radiation intensity information to achieve enhanced classification effects, Effects of Simplifying Power Prediction Problems and Improving Prediction Accuracy

Active Publication Date: 2017-03-22
WUHAN UNIV
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Problems solved by technology

Due to the high cost of numerical weather forecasting, it is difficult to be widely used in distributed photovoltaic forecasting systems. Most current research uses a combined forecasting method that classifies different types of data based on weather forecast information and cloud cover information, and does not fully exploit historical solar radiation intensity information. Accuracy drops significantly with inaccurate weather forecasts

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  • Distributed photovoltaic ultra-short-term forecasting method based on Adaboost clustering and Markov chain
  • Distributed photovoltaic ultra-short-term forecasting method based on Adaboost clustering and Markov chain
  • Distributed photovoltaic ultra-short-term forecasting method based on Adaboost clustering and Markov chain

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Embodiment 1

[0102] In order to improve the prediction accuracy under non-sunny conditions, a distributed photovoltaic ultra-short-term prediction method based on Adaboost clustering and Markov chain is proposed, and the deterministic component of the light intensity sequence is extracted by using the moving average method, and different weather types are obtained through statistical analysis. The attenuation factor of the light intensity; the Adaboost improved KNN method is used to cluster and analyze the historical data to establish a classification model; combined with the multi-order weighted Markov chain based on the error sequence to realize the prediction of the surface light intensity in the future, and finally through the photoelectric conversion model Realize ultra-short-term forecasting of distributed photovoltaic power generation.

[0103] Embodiment 1 is solved through the following technical solutions:

[0104] A distributed photovoltaic ultra-short-term prediction method bas...

Embodiment 2

[0149] A distributed photovoltaic ultra-short-term prediction method based on Adaboost clustering and Markov chain, such as figure 1 shown, including the following steps:

[0150] ①Using the moving average method to extract the deterministic component of the light intensity sequence, statistical analysis to obtain the light intensity attenuation factors of different weather types.

[0151] In Example 2, in order to extract the deterministic components of the light intensity sequence and reflect the average change trend of the light intensity, a time window of 30 minutes is selected for smoothing. Taking the actual data as an example, the processing effect is as follows figure 2 shown. According to the statistics of historical record data from 2015 to 2016 and corresponding standard sunny model data, the frequency distribution of attenuation values ​​under four typical weather types (sunny, cloudy, rainy, and cloudy) is obtained as follows: image 3 As shown, the distributio...

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Abstract

The invention relates to the technical field of distributed photovoltaic power generation systems, and particularly relates to a distributed photovoltaic ultra-short-term forecasting method based on Adaboost clustering and Markov chains. The method comprises the following steps: 1, extracting deterministic components of illumination intensity sequences by using a sliding average method, and implementing statistical analysis to obtain illumination intensity attenuation factors of different weather types; 2, implementing clustering analysis on historical data by using an Adaboost improved KNN method to establish a classification model; 3, forecasting the solar irradiance of the earth surface by using a multistage weighted Markov chain method; and 4, establishing a photoelectric conversion model to complete the ultra-short-term forecasting of photovoltaic power. According to a combination forecasting method for implementing feature extraction and data mining on input data, provided by the invention, after the historical photovoltaic output data is classified according to the typical weather type, the state of the forecasting process is refined by introducing the weather type attenuation factors, so that better forecasting effects can be achieved in sunny weather, and the forecasting precision and accuracy in non-sunny weather can also be increased.

Description

technical field [0001] The invention belongs to the technical field of distributed photovoltaic power generation systems, and in particular relates to a distributed photovoltaic ultra-short-term prediction method based on Adaboost clustering and Markov chains. Background technique [0002] In recent years, the installed capacity of photovoltaic power generation has continued to increase, and its volatility and intermittency have increasingly prominent impacts on the operation of power systems. The ultra-short-term prediction of distributed photovoltaics is of great significance to the safe and economic operation of the power system, which is mainly reflected in two aspects: first, formulate control strategies based on the predicted power, reduce the impact of power fluctuations on the power system when photovoltaics are connected to the grid, and improve the efficiency of the system. Safety, reliability and controllability; second, it helps the power system dispatching depar...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06K9/62
CPCG06Q10/06375G06F18/2321
Inventor 邓长虹谭津李丰君
Owner WUHAN UNIV
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