Power sales amount intelligent prediction method based on deep recurrent neural network

Abstract

The invention relates to big data processing, and aims to provide a power sales amount intelligent prediction method based on a deep recurrent neural network. The method comprises the steps of readinghistorical data of sales flow and electricity consumption of an electric power department, performing information mining and analysis after preprocessing, and evaluating a relation between the amountpayment time and user payment time to obtain distribution information; organizing a historical data structure, taking normalized n-day data as input, learning high-dimensional characteristics by using a multi-layer recurrent neural network (GRU), and inputting the high-dimensional characteristics into a softmax discriminator to carry out sale amount level classification in a certain period of time; and traversing hyper-parameters of the deep circulation network model by using a grid method, recording the optimal hyper-parameters after multiple experiments, constructing a final amount prediction deep circulation neural network model, and intelligently predicting the electric power sales amount by using the final amount prediction deep circulation neural network model. The method is more accurate and reasonable, manual intervention is less, the result is more robust, the method is more suitable for big data, and automatic learning can be realized.

Description

technical field [0001] The invention relates to big data processing, in particular to an intelligent prediction method for electricity sales amount based on a deep cyclic neural network. Background technique [0002] Sales forecasting refers to the sales forecasting model obtained through mathematical modeling based on the past sales situation and the analysis of the future form, and on the basis of fully considering various influencing factors, so as to realize the sales of all products or specific products within a certain period of time in the future. Estimates of quantities and sales amounts. Sales forecast is very important for the enterprise's development planning, strategic deployment, production management, import, export and effective control of each link of the supply chain. There are many factors that affect the sales forecast, including market demand, development status of related enterprises, policy changes and seasonal changes, etc. Among these many factors, ...

AI Technical Summary

Problems solved by technology

In fact, it is difficult to apply deep recurrent neural network technology to the daily sales forecast of the power system. The main reason is that the historical data used for forecasting only have a single feature of sales amount and sales date, and the feature dimension is too large. Less makes the model overfitting The data distribution seen cannot predict the unknown data well

Images