Construction method of multi-time scale time series collaborative prediction model

Abstract

The invention discloses a construction method of a multi-time scale time series collaborative prediction model, which comprises the following steps: 1) inputting recording data generated by the powerconsumption condition of a user with the change of time, and constructing a relevant time sequence representation according to the recording data; 2) based on the time sequence representation obtainedin the step 1), analyzing all the power consumption data, capturing different characteristics and analyzing the corresponding variation rules, and thereby constructing a time scale matrix sequence; 3) according to the output of the step 1) and the step 2), constructing a prediction model of the time series of the power consumption data, wherein the prediction model of the time series is a multi-scale RNN model; 4), according to the output of step 1), step 2) and step 3), and the output of external factors, the weighted fusion solution is carried out to obtain a multi-time scale power consumption sequential data cooperative prediction model. The method of the invention enables the accuracy of the power consumption prediction of the user to be improved.

Description

technical field [0001] The invention relates to a method for constructing a collaborative prediction model of multi-time scale power consumption time series data. Background technique [0002] With the rapid development and extensive application of power system-related technologies, a large amount of power consumption information related to power users is generated in large quantities. These time data generated over time contain user behavior characteristics and development rules. For these data Accurate analysis and prediction have important guiding significance for power grid planning and management decision-making of economic departments. At present, the methods for forecasting electricity consumption are divided into two categories, mainly including traditional forecasting methods and forecasting methods based on artificial intelligence. [0003] Traditional forecasting methods mainly include methods based on probability box theory, random forest algorithm, autoregressi...

AI Technical Summary

Problems solved by technology

This type of method can better solve the problems of nonlinearity and high latitude in traditional algorithms, but most of the models are only studied on a single time scale, and less consideration is given to the relationship between power consumption in different time series. dependencies

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