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Short-term photovoltaic power generation prediction method considering correlation degree of weather and meteorological factors

A technology of meteorological factors and photovoltaic power generation, applied in forecasting, neural learning methods, calculations, etc., can solve the problem of low accuracy in historical day selection

Active Publication Date: 2020-06-09
YANSHAN UNIV
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Problems solved by technology

By proposing the correlation between different weather and meteorological factors and photovoltaic power generation, the problem of low accuracy in historical day selection is solved; and the bad data in the collected data is eliminated through the iForest algorithm

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  • Short-term photovoltaic power generation prediction method considering correlation degree of weather and meteorological factors
  • Short-term photovoltaic power generation prediction method considering correlation degree of weather and meteorological factors
  • Short-term photovoltaic power generation prediction method considering correlation degree of weather and meteorological factors

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Embodiment Construction

[0076] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0077] Such as figure 1 and figure 2 As shown, the present invention discloses a short-term photovoltaic power generation forecast that takes into account the degree of correlation between weather and meteorological factors, which includes the following steps:

[0078] S1. Divide weather types into clear sky, cloudy weather, haze weather, and rainy weather, and meteorological factors into solar radiation intensity, temperature, wind speed, air relative humidity, and atmospheric aerosol index, standardize the data, and remove them by iForest algorithm bad data;

[0079] S2. Find the Pearson correlation coefficient between the photovoltaic power generation power and each meteorological factor under different weather types, and normalize the Pears...

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Abstract

The invention discloses a short-term photovoltaic power generation prediction method considering the correlation degree of weather and meteorological factors. The method comprises the following steps:1, rejecting bad data through an iForest algorithm; 2, respectively calculating Pearson correlation coefficients R of the photovoltaic power generation power and the five meteorological factors underfour weather types, and normalizing the Pearson correlation coefficients R; 3, performing fuzzy clustering on the five meteorological factors of the to-be-measured day, and obtaining a correlation coefficient of the historical day and the to-be-measured day; 4, a correlation coefficient normalization value is introduced, and the correlation degree between a historical day and a to-be-measured dayis solved; and 5, taking the historical day with high correlation degree as historical data, inputting the historical data and the meteorological factors of the day to be measured into the improved ACO-BP neural network, and finally obtaining a predicted value of the solar photovoltaic power generation to be measured; and 6, determining a neural network correlation coefficient, and performing simulation. The method aims to improve the photovoltaic power generation prediction precision, improves the practicality of a prediction model, and plays a great role in the combination of scheduling andprediction.

Description

technical field [0001] In the technical field of new energy power generation prediction, the present invention specifically proposes a short-term photovoltaic power generation prediction method that takes into account the correlation between weather and meteorological factors. Background technique [0002] With the huge increase in world energy consumption and the rapid reduction of coal and other resources, human demand for clean and renewable energy is increasing. As a clean, environmentally friendly and renewable new energy, solar energy has become One of the important choices for energy saving and emission reduction. A large number of photovoltaic systems are connected to the grid, and their own intermittent and random problems will have a negative impact on the stability of the grid connected to it. Therefore, accurate prediction of photovoltaic power generation plays an irreplaceable and important role in rational dispatching of power grids, peak shaving and valley fi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06N3/12G06Q50/06
CPCG06Q10/04G06Q50/06G06N3/126G06N3/08G06N3/044G06N3/045Y04S10/50
Inventor 钟嘉庆陈博高帆帆张晓辉
Owner YANSHAN UNIV
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