A short-term load forecasting method based on improved HS-NARX neural network

Abstract

The invention discloses a short-term load forecasting method based on improved HS-NARX neural network, the method comprises S1 collecting data and preprocessing; S2, establishing NARX neural network;the neural network is trained with the preprocessed data. 3, determining a fitness function of the HS algorithm; S4, setting parameters of the harmony search algorithm; S5 initialization parameters; S6 generates HMCR and PAR according to HMCRmean and PARmean, and the pitch adjustment bandwidth is (BWmax, BWmin); S7 generating (0, 1) random numbers, generating new harmonic vectors, and uses improved pitch adjustment rules and adaptive parameter tuning method to generate new harmonics; S8, comparing the generated new solution with the worst solution in the harmonic memory bank, if the new solution is better than the worst solution, replacing the worst solution, otherwise, not operating, recording HMCR and PAR again; S9 returning to S7 if the number of iterations is not reached, otherwise, the optimal solution is outputted; S10 mapping the optimal solution to the neural network, obtaining the weights W and the threshold theta of each layer of the network, and training the network and theload forecasting.

Description

technical field [0001] The invention relates to the technical field of electric power systems, in particular to an electric short-term load forecasting method based on an improved HS-NARX neural network. Background technique [0002] As one of the important tasks for the stable operation of the power system, the research on power load forecasting has been widely valued by scholars and practical work. The research on short-term load forecasting is of great significance to the safety, stability and economic operation of the power grid. First of all, accurate short-term load forecasting research is helpful for power grid enterprises to reasonably arrange the start and stop of generating units to ensure the stability of power grid operation; According to the basis, the economic benefits of power grid operation are improved; thirdly, short-term load forecasting is an important basis for power grid energy-saving dispatching, and it is also the basis for ensuring the safety and re...

AI Technical Summary

Problems solved by technology

And because the NARX neural network prediction method is easy to fall into local solutions and the convergence speed is slow, in order to improve the accuracy of short-term power load forecasting, an improved harmony search algorithm (HS) is proposed to optimize the initial weight and threshold of the NARX neural network

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