Wind power output prediction method based on historical predicted value

Abstract

The invention relates to the technical field of wind power prediction, in particular to a wind power output prediction method based on a historical predicted value. The method comprises the following specific steps: S1, building a BP neural network model, and carrying out the data quality analysis of historical measured data; s2, verifying BP neural network example indexes according to the data RMSE and nMAE; s3, after the index verification is passed, fragmenting the collected long-term historical data according to time, optimizing a neuron threshold by using a particle swarm algorithm, and performing network iteration neuron parameter correction by using a historical data fragmentation iteration process; and S4, after the parameter model is corrected, determining a predicted output time period according to an actual demand, carrying out time sequence series connection on historical data and a wind power prediction value of the wind power plant, taking a series connection result as input of the neural network model, and carrying out actual prediction value output and verification. The predicted value is closer to the actual value, and the requirements of current development are met.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a wind power output forecasting method based on historical forecast values. Background technique [0002] Although new energy has the advantages of environmental protection and flexibility, due to its special network structure, after being connected to the power grid, the traditional radial power grid structure will become an interactive network of power supply and user interconnection. The power flow in the grid no longer flows unidirectionally from the grid to the load as in the past, and it may also flow reversely from the distributed new energy source on the user side to the grid. This uncertain power flow direction will make the load of the power grid more uncertain and thus more difficult to predict. The access of a large number of new energy sources not only complicates the forecasting process of power demand, but also makes it difficult for grid planners t...

AI Technical Summary

Problems solved by technology

This uncertain power flow direction will make the grid load more uncertain and thus more difficult to predict

The access of a large number of new energy sources not only complicates the forecasting process of power demand, but also makes it difficult for grid planners to grasp the growth of load

In addition, since wind power generators are generally connected to the distribution network, due to the strong influence of the weather environment and the randomness of the power supply, this increases the randomness of the grid load and makes power load forecasting more difficult.

At the same time, the low accuracy of load forecasting also brings many difficulties to power grid planning, load analysis and forecasting work and marketing work.

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