User electricity consumption prediction method based on Prophet-LSTM model

Abstract

The invention discloses a user electricity consumption prediction method based on a Prophet-LSTM model, and the method comprises the following steps: S1, obtaining the historical data of the electricity consumption of a user through an intelligent electric meter, wherein the historical data comprise time series data, weather and temperature data, and holiday and festival data; S2, preprocessing and normalizing the historical data, wherein the original power consumption data is X={x1, x2,..., xn}, and the preprocessing of the original data comprises the processing of missing values, abnormal values, repeated values and invalid values; S3, constructing a Prophet prediction model, inputting the processed historical electricity consumption data X'={x'1, x'2,..., x'n} into the Prophet model, and performing Prophet prediction; S4, in order to prevent prediction overfitting, performing combined prediction in combination with an improved long and short term memory (LSTM) network model; and S5, measuring and verifying the fitting degree and the prediction effect of the combined model, and using common evaluation indexes. According to the method, the characteristics and rules of the power consumption data are analyzed, the accuracy of the prediction model is improved, and the method has important guiding significance for making effective power supply services by the state grid and each power supply company.

Description

technical field [0001] The invention relates to the fields of time series analysis and energy consumption prediction, and specifically relates to a method for predicting user electricity consumption based on the Prophet-LSTM model. Background technique [0002] Analyzing and predicting the energy consumption of users can provide the national grid or power supply company with the ability to judge whether there is an abnormal situation in the user's electricity consumption and provide corresponding solutions. The relevant power supply company can refer to the predicted trend of power consumption and adjust the power supply in time Decision-making plan plans to improve the efficiency and reliability of power supply services, promote the development of awareness of energy conservation and emission reduction, and build an electricity-saving society. Many scholars have done some research in this area, but the user's electricity consumption prediction is affected by many factors su...

AI Technical Summary

Problems solved by technology

It has the advantages of simple operation, low complexity of parameter model, short calculation and prediction time, and good prediction effect, etc., and it quickly became popular in various fields. However, the Prophet model has the disadvantage of falling into overfitting at special time points. There are also deficiencies in composite features. Therefore, for the disadvantages of a single prediction model, it will lead to poor prediction results. This paper proposes a strategy of combining the improved LSTM model with the Prophet model to combine the advantages of the two models to reduce prediction errors.

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