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Short-term photovoltaic power generation prediction method and system

A technology of photovoltaic power generation and forecasting methods, applied in forecasting, data processing applications, instruments, etc., can solve problems such as limited forecasting accuracy and weak ability to change meteorological characteristics, and achieve the effect of improving accuracy and reducing modal aliasing

Active Publication Date: 2019-10-11
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the inventors found that the currently proposed physical prediction methods and data-driven prediction methods have limited prediction accuracy and weak ability to cope with changes in meteorological characteristics

Method used

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  • Short-term photovoltaic power generation prediction method and system
  • Short-term photovoltaic power generation prediction method and system
  • Short-term photovoltaic power generation prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] figure 1 A flow chart of a short-term photovoltaic power generation forecasting method in this embodiment is given.

[0044] Such as figure 1 As shown, a short-term photovoltaic power generation prediction method in this embodiment includes:

[0045] S101: Obtain the power generation and weather history data of the photovoltaic power station at the same time, and construct a training set and a test set.

[0046] Among them, the meteorological historical data include solar irradiance (G), air temperature (T), cloud type (CT), dew point (DP), relative humidity (RH), precipitable water (PW), wind direction (WD), wind speed ( WS) and air pressure (AP).

[0047] S102: Separately group the training set and the testing set according to preset time intervals.

[0048] Divide the samples in the training set and the test set into groups according to the preset time interval;

[0049] The preset time interval can be 1h.

[0050] It should be noted that the preset time interv...

Embodiment 2

[0090] Such as image 3 As shown, a short-term photovoltaic power generation prediction system in this embodiment includes:

[0091] (1) Training set and test set construction module, which is used to obtain the power generation and weather history data of the photovoltaic power station at the same time, and construct the training set and test set.

[0092] Among them, the meteorological historical data include solar irradiance (G), air temperature (T), cloud type (CT), dew point (DP), relative humidity (RH), precipitable water (PW), wind direction (WD), wind speed ( WS) and air pressure (AP).

[0093] (2) A grouping module, which is used to group the training set and the test set respectively according to a preset time interval.

[0094] Divide the samples in the training set and the test set into groups according to the preset time interval;

[0095] The preset time interval can be 1h.

[0096] It should be noted that the preset time interval may also be half an hour, an...

Embodiment 3

[0140] A computer-readable storage medium in this embodiment, on which a computer program is stored, and when the program is executed by a processor, the following figure 1 The steps in the short-term photovoltaic generation forecasting method are shown.

[0141] In this embodiment, the trained deep LSTM sequence neural network model is used to predict the power generation of short-term photovoltaic power generation, which better fits the nonlinearity of the data and improves the accuracy of power generation prediction.

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Abstract

The invention provides a short-term photovoltaic power generation prediction method and system. The short-term photovoltaic power generation prediction method comprises the following steps: acquiringpower generation power and meteorological historical data of a photovoltaic power station at the same moment, and constructing a training set and a test set; grouping the training set and the test setaccording to a preset time interval; independently clustering the samples of each group of training set and test set into sample data of a preset number of weather types; generating power data in thesample data of each weather type is decomposed step by step according to different fluctuation scales by adopting an NACEMD signal decomposition algorithm to obtain components with different time-frequency characteristics, and then combining the components with corresponding weather type characteristics to construct characteristic vectors of each group of training set and test set; training and testing the deep LSTM sequence neural network model by using the feature vectors of the training set and the test set respectively; and predicting the generated power of short-term photovoltaic power generation by using the trained deep LSTM sequence neural network model.

Description

technical field [0001] The disclosure belongs to the field of photovoltaic power generation, and in particular relates to a short-term photovoltaic power generation prediction method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] As the main source of renewable energy growth, solar energy is changing from a marginal energy source to a major energy source. In fact, it is becoming the main source of electricity supply in many developed countries, especially in Europe. However, compared with traditional energy sources, solar energy is intermittent and unstable, and it is greatly affected by weather conditions. The entry of solar energy into the energy system will have a negative impact on the operation of the system. [0004] Accurate forecasting of photovoltaic power generation is critical for the safe and smooth operation of...

Claims

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Application Information

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IPC IPC(8): G06Q50/02G06Q10/04G06N3/04
CPCG06N3/049G06Q10/04G06Q50/02
Inventor 孙波周宝斌张承慧
Owner SHANDONG UNIV
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