A combination forecasting method of wind power time series

Abstract

The invention discloses a wind power time series combined prediction method. The method is based on a direct multi-step prediction method and a rolling multi-step prediction method for performing combined prediction on day-ahead wind power of a wind power plant. By adopting the wind power time series combined prediction method disclosed by the invention, day-ahead prediction precision of the wind power prediction method can be effectively improved, the problem of poor wind power prediction effect in an existing engineering wind power short-term prediction technology is solved, purchase cost for huge spare power generation capacity can be reduced for a power grid enterprise and the operation benefits of the power grid enterprise are upgraded; meanwhile, abandoned wind power can be effectively reduced, wind power effective grid-connected capacity can be upgraded, huge profits are brought to a power generation enterprise and energy conservation and emission reduction are promoted.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to a combined forecasting method of wind power time series. Background technique [0002] In the context of the global energy crisis and environmental crisis, wind power generation will be one of the most competitive renewable energy sources in the next few decades. The wind energy reserves are huge, and using wind energy to generate electricity can not only reduce environmental pollution, but also reduce the cost of the power system. fuel costs, bringing considerable economic benefits. [0003] By the end of 2010, the total installed capacity of wind power in my country surpassed that of the United States, ranking first in the world; as of December 2014, the installed capacity of grid-connected wind power in Jiangsu Power Grid had reached 2.97 million kilowatts, accounting for about 3.7% of the total installed capacity of...

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Problems solved by technology

However, the prediction accuracy of the existing wind power prediction methods is still not high. The error of the international advanced wind power prediction system is about 15%, which cannot meet the needs of the project.

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