Analysis method applied to luminous energy power generation prediction

Abstract

The invention discloses an analysis method applied to luminous energy power generation prediction. The method comprises the following steps: 1) acquiring luminous energy power generation data; 2) extracting and processing luminous energy power generation data characteristics; 3) constructing a random multi-scale Bayesian distribution regression model based on a multi-core method and a Bayesian framework; and (4) conducting quantitative analysis and prediction on the luminous energy power generation data. The method can achieve steady quantitative analysis and prediction on the luminous energypower generation data.

Description

technical field [0001] The invention relates to the field of photovoltaic power generation, in particular to an analysis method applied to the prediction of photovoltaic power generation. Background technique [0002] In contemporary production and life, the consumption of energy is increasing day by day, which urgently requires the development of new energy sources. Among them, solar energy, as a new energy source, has been widely used in production and life. In the final analysis, the energy of solar energy is light energy, which is greatly affected by the weather and has strong randomness and volatility. At present, the proportion of photovoltaic power generation in the power grid is very small, and these characteristics will not bring obvious adverse effects on the power grid. However, as the installed capacity of photovoltaic power generation continues to expand, its proportion in the grid is also increasing year by year. The photovoltaic power station connected to th...

AI Technical Summary

Problems solved by technology

However, as the installed capacity of photovoltaic power generation continues to expand, its proportion in the grid is also increasing year by year. The photovoltaic power station connected to the grid will bring hidden dangers to the safe and stable operation of the power system and the quality of power energy. Therefore, it is necessary In-depth research on PV output forecasting

[0003] At present, there are many research methods for photovoltaic power generation forecasting, and the realization of long-term forecasting is relatively easy, while short-term forecasting is greatly affected by meteorological factors, and accurate forecasting is difficult

Images