Microgrid photovoltaic power station generating capacity combined forecasting method

Abstract

A microgrid photovoltaic power station generating capacity combined forecasting method belongs to the new energy power generation output forecasting field. The method comprises the following steps: 1) taking generating capacity data of a photovoltaic power station in a microgrid as input / output data of a forecasting model; 2) carrying out generating capacity forecasting through wavelet transform and a BP neural network; 3) carrying out generating capacity forecasting by utilizing a particle swarm algorithm to optimize a support vector machine; 4) obtaining model test weights by utilizing a variance-covariance combination method; and 5) calculating combined forecasting result according to the weights and model output corresponding to the new input. Compared with an existing forecasting method, the forecasting method starts from a new angle, avoids considering complex external factors and adopts a combination method, and has the advantages of being good in real-time performance, high in accuracy and high in adaptability and the like.

Description

technical field [0001] The invention relates to the technical field of prediction of new energy power generation output, in particular to a combined prediction method for power generation of a microgrid photovoltaic power station using a neural network and a support vector machine. Background technique [0002] With the depletion of fossil energy and the enhancement of human awareness of environmental protection, the issue of energy and electricity has become more and more important. As a new energy source, especially the combination and supply of electricity, microgrid has become a research hotspot in recent years. Microgrid combines distributed power, energy storage, load and control devices to form a unified and controllable small power generation and distribution system, which is an effective means for distributed power to connect to the grid and promotes the development of distributed power generation technology Development has broad prospects. [0003] Solar photovolt...

AI Technical Summary

Problems solved by technology

The principle prediction method and regression prediction method are difficult to reflect the dynamic and nonlinear relationship between power generation and weather and other factors, and the prediction effect on complex and changeable photovoltaic power generation systems is poor; artificial intelligence and new theoretical methods mostly use a single prediction model , and considering complex external interference factors, such as radiation intensity, radiation angle, temperature, cloud cover, etc., the prediction accuracy and flexibility are limited, the original data is complicated, and data analysis and acquisition are difficult

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