A Combination Forecasting Method of Electric Power Load Based on Neural Network Model

Abstract

The invention discloses an electric load combination forecasting method based on a neural network model, which includes collecting multiple power consumption index features of each user to form an initial data set; standardizing the index data in the initial data set to obtain standard data set as sample points; input the standard data set into the clustering model to extract the characteristic features of multiple standard data sets; input the original features and representative features of the data set into the neural network, corresponding to the output power load value, and train to obtain the initial neural network prediction model ; Optimize the parameters of the initial neural network forecasting model to obtain the best power load combination forecasting neural network. The technical scheme of the present invention aims at the fact that the current power load forecasting method is not accurate when faced with multi-data set power load forecasting, and adopts a combined forecasting method that combines the clustering algorithm with the cyclic neural network model, and the clustering algorithm Optimizing with recurrent neural network improves the accuracy of power load forecasting.

Description

technical field [0001] The invention belongs to the field of electric load forecasting, and in particular relates to an electric load combination forecasting method based on a neural network model. Background technique [0002] With the modernization of the power system, the goal of the power system in the information age is to provide high-quality, high-reliability, high-efficiency, and low-cost power services for each user. In order to achieve this goal, an intelligent power system should be able to grasp and understand each user's electricity consumption habits, and grasp the user's electricity demand in advance, so as to scientifically and efficiently allocate electricity according to the needs of each user in different power consumption periods. powered by. Therefore, accurate forecasting of electricity load is very necessary. In practice, power load forecasting can help us understand the potential power consumption patterns in a certain area, such as peak and low pea...

AI Technical Summary

Problems solved by technology

However, since the k-means algorithm has the problem of k value and k center point selection optimization, it cannot meet the current needs in the face of multi-data set prediction.

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