Wind farm power combination prediction modeling method based on gray relational degree analysis

Abstract

The invention discloses a combination forecast modeling method of wind farm power by using gray correlation analysis, belonging to the technical field of wind power generation modeling. In particular, the invention is related to a weighted combination forecast method of wind power based on a least square support vector machine and an error back propagation neural network. The forecast method comprises that forecasted values of wind speed and wind direction are acquired in advance from meteorological departments while real-time output power is acquired from a wind farm data acquiring system; that the forecasted values of wind speed and wind direction and the real-time output power are inputted into a data processing module for data analyzing extraction and data normalization, and then normalized data is loaded to a database server; processed data in the database server is extracted by a combination forecast algorithm server to carry out model training and power forecast, and the wind farm sends running data to the data processing module in real time to realize rolling forecasting. The method of the invention achieves the goal of combination forecast of wind farm output in a short time. The method not only maximally utilizes advantages of two algorithms but also increases forecast efficiency by saving computing resources and shortening computing time.

Description

technical field [0001] The invention belongs to the technical field of wind power generation modeling, in particular to a wind farm power combination prediction modeling method using gray relational degree analysis. Specifically, it is a weighted combination prediction method of wind power based on least squares support vector machine and error backpropagation neural network. Background technique [0002] In today's energy shortage, it is becoming more and more important to develop renewable energy power generation, especially wind power generation and maximize its power generation. However, the inherent volatility, instability and intermittency of wind energy make the output of wind power fluctuate with the change of wind speed all the time. If the real-time output of wind farms is incorporated into the power grid to participate in the operation of the power market, it will have an impact on the smooth and healthy operation and dispatch of the power grid. Two commonly use...

AI Technical Summary

Problems solved by technology

[0003] Most of the existing forecasting systems in China use time series methods based on linear models or single artificial intelligence modeling. The disadvantages are: the application of linear models can only represent the linear relationship between input and output, and the output of wind farms with time The transformation presents a certain volatility and nonlinear relationship, which leads to the limitation of purely using the linear model to complete the prediction

At present, the most widely used artificial intelligence model is the artificial neural network. While it has the advantages of fast operation speed and high precision, it is easy to fall into the local minimum value, which makes it difficult to obtain the global optimal solution in the prediction process.

The emergence and development of support vector machines solves this problem, but at the same time, it also has limitations in outputting uncertain information.

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