Short-term load predicting method of power grid

Abstract

The invention relates to a short-term load predicting method of a power grid. The method comprises the steps: step 1, acquiring historical data and pre-treating the data; step2, decomposing the historical load sample data into a plurality of different-frequency sub-sequences by using wavelet decomposition; step 3, performing single-branch reconstruction to each sub-sequence; step 4, dynamically choosing training samples and establishing a neural network predicting model optimized by a vertical and horizontal intersection algorithm; step 5, predicting each sub-sequence 24 hours in advance by using the optimal neural network predicting model; and step 6, superposing the predicted value of each sub-sequence to obtain a whole prediction result. The inherent defects of the neutral network can be overcome by optimizing BP neutral network parameters by a brand-new swarm intelligence algorithm, that is, the vertical and horizontal intersection algorithm instead of the traditional algorithm; the burr problem caused by the impact load processing is solved by the wavelet decomposition, the precision declining resulting from the removal of the effective load in the burr pre-treatment is solved and the predicted value of the hybrid algorithm is more approximate to the actual measured load value.

Description

technical field [0001] The invention relates to a short-term load forecasting method of a regional power grid, in particular to a short-term load forecasting method of a power grid using a hybrid wavelet transform and a crossover algorithm to optimize a neural network. Background technique [0002] At present, the most widely used method in load forecasting is artificial neural network forecasting. Among them, BP neural network has been widely used in the field of forecasting because of its self-organization and self-learning ability, which can realize any nonlinear mapping from input to output. However, the BP algorithm uses the gradient descent method to adjust the weights and thresholds, resulting in slow convergence and easy to fall into local optimum. As more influencing factors and learning samples are taken into consideration, the amount of calculation and the number of weights of the neural network will increase dramatically. In addition, when a large number of...

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