Method and system for predicting load of power distribution network

Abstract

The present invention relates to a method and a system for predicting the load of a power distribution network. The method comprises the steps of obtaining a unsupervised training sample set, a supervised training sample set and a test sample set according to the time information and on the basis of the historical load influence factor of the power distribution network and the historical load value of a to-be-predicted area; according to the unsupervised training sample set, subjecting a DBN model layer in a pre-established load prediction model to unsupervised training layer by layer, wherein the load prediction model includes a DBN model layer and a linear neural network layer; adopting network parameters obtained through during the unsupervised training step as the network parameter initial values of the load prediction model; according to the supervised training sample set, subjecting the load prediction model to supervised training, and obtaining an optimal load prediction model; testing the test sample set by using the optimal load prediction model so as to obtain a load prediction value of the to-be-predicted area. The present invention can realize the high-precision loadprediction of the intelligent power grid environment under the influence of various factors.

Description

technical field [0001] The invention relates to the technical field of distribution network, in particular to a method, system, storage medium and computer equipment for distribution network load forecasting. Background technique [0002] With the development of smart grids, large-scale renewable distributed power access has increased the volatility of self-consumption loads, and the wide application of electric vehicles has led to uncertainty in the location of charging loads. Behavior, users' psychological reaction to incentive policies, and urban development conditions all have a huge impact on power consumption patterns; therefore, accurate load forecasting is the basis for realizing safe and economical operation of distribution networks and scientific management of power grids, as well as improving the utilization rate of power generation equipment and An important guarantee to improve the effectiveness of economic dispatch. [0003] The existing load forecasting metho...

AI Technical Summary

Problems solved by technology

The model of this type of method is simple, but it can only process a small number of influencing factors and sample data, and has high requirements for the stationarity of the original time series

Artificial intelligence methods include artificial neural network (Artificial Neural Network, ANN) and support vector machine (Support Vector Machine, SVM). The disadvantage of falling into a local minimum; although the SVM method can better solve the problems of small samples, nonlinearity, high-dimensionality, local minimum, etc. etc., there are disadvantages such as slow convergence speed and low prediction accuracy

[0004] Therefore, the existing load forecasting methods are difficult to achieve high-precision load forecasting that is influenced by various factors in the smart grid environment.

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