Photovoltaic power generation power day-ahead prediction method based on image feature extraction

Abstract

The invention relates to a photovoltaic power generation power day-ahead prediction method based on image feature extraction. The method comprises the steps of firstly collecting historical data of each influence factor of photovoltaic power generation, wherein the historical data comprises temperature, clearness index, PM index, power generation power, humidity, wind speed and the like; constructing a column matrix taking each influence factor as a row, and taking matrix elements as picture pixel points to obtain a plurality of two-dimensional images; carrying out normalization processing onthe image according to rows, setting convolution kernel dimensions and number, moving strides and filling dimensions, dividing photovoltaic power generation power into a plurality of gears according to accuracy, and carrying out category marking on the gears; and extracting image features based on a convolutional neural network principle, and establishing a prediction model to realize photovoltaicpower generation power day-ahead prediction. According to the photovoltaic power generation power prediction method provided by the invention, the prediction speed is improved while the prediction precision is improved, the influence of photovoltaic power generation power fluctuation on a power grid is reduced, and the photovoltaic power generation power prediction method has important significance for popularization and application of photovoltaic power generation.

Description

technical field [0001] The invention belongs to the field of photovoltaic new energy, and relates to a new method for predicting photovoltaic power generation power in advance based on image feature extraction. Background technique [0002] Solar energy is one of the most promising renewable energy sources for large-scale power generation, but due to its intermittency and variability, the output power presents characteristics such as intermittent and randomness. On the one hand, large-scale grid-connected operation will have a great impact on the security of the traditional power grid and system reliability; on the other hand, when the regional load power supply is borne by the micro-grid, since the micro-grid generally has a small capacity, the solar power Even a small ratio may bring a series of problems to the stable operation of the microgrid. In order to ensure the reliable operation of modern power systems and realize economic dispatch, it is necessary to accurately p...

AI Technical Summary

Problems solved by technology

The regression model adopts a pre-selected mathematical model structure. When the model does not match the characteristics of photovoltaic data, the prediction accuracy is difficult to guarantee; traditional neural networks generally require a large number of training samples, and the update of neuron synaptic coefficients in the training process requires a long time to converge time, so it can generally only meet the offline prediction requirements of photovoltaic power generation

Images