Recurrent neural network short-term power load prediction method of improved whale algorithm

Abstract

The invention discloses a recurrent neural network short-term power load prediction method for improving a whale algorithm, and relates to the technical field of short-term power load prediction. A recurrent neural network is used for short-term power load prediction, similar daily load data of a day to be predicted is used as input data of the recurrent neural network, and the number of input neurons, the number of output neurons, the number of hidden layers, the learning rate and the gradient descent algorithm of the recurrent neural network are determined. And a prediction model of the recurrent neural network is constructed. And the whale optimization algorithm is improved by using a differential evolution algorithm, so that the high-dimensional global optimization capability of a common whale algorithm is improved. An improved whale algorithm is adopted to pre-train the weight in the recurrent neural network, after pre-training is finished, the trained weight is put into a recurrent neural network model, then a gradient descent algorithm is adopted to train the recurrent neural network model, and after training is finished, a neural network model with the fixed weight is obtained, and then load prediction is carried out.

Description

technical field [0001] The invention relates to the technical field of short-term power load forecasting. Background technique [0002] With the wide application of electric energy, the economic development of all countries in the world is increasingly dependent on electric power, and the demand for electric energy and the quality of power consumption are also getting higher and higher. Due to the particularity of electric energy, it cannot be stored in large quantities and needs to be used immediately. To establish a balance state between the production, transmission and use of electric energy, it is necessary to accurately estimate the load consumption in the power system to ensure the reliability of the power system. performance, optimize the dispatching of the power system, and improve economic benefits. [0003] Chen Gang, Zhou Jie, Zhang Xuejun, et al. Daily load forecasting based on BP and RBF cascaded neural network [J]. Power Grid Technology. 2009,33(12):101-105. C...

AI Technical Summary

Problems solved by technology

[0006] The main problems of the method proposed in the above literature are two points. One is to use a neural network without memory capacity for load forecasting, and the results of load forecasting may produce discontinuity; the other is to use a single gradient descent algorithm to predict Weight training, the neural network is easy to fall into a local optimal state and it is difficult to jump out, which affects the prediction accuracy

Images