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

A technology with multiple time scales and construction methods, which is applied in the field of collaborative forecasting model construction of multi-time scale power consumption time series data, can solve problems such as single time scale and less dependencies, and achieve the effect of improving accuracy

Active Publication Date: 2019-03-01
STATE GRID TIANJIN ELECTRIC POWER +2
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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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  • Construction method of multi-time scale time series collaborative prediction model
  • Construction method of multi-time scale time series collaborative prediction model

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Embodiment Construction

[0032]Time series data of electricity consumption is a collection of user electricity consumption data at different points in time. It is a data type that widely appears in various fields of production and life. It reflects the state changes and Development Law. Because time-series data of electricity consumption is ubiquitous in people's life, the prediction of electricity consumption data is also the focus of many research work. Electricity time-series data forecasting is a method of predicting its future change trend by fitting a specific prediction model according to the development process and laws reflected in the known electricity consumption sequence. Building an accurate time series data prediction model of electricity consumption is helpful to resource scheduling and management, social security and avoiding resource waste, etc., which is of great significance in real life.

[0033] The method for constructing the multi-time-scale power consumption time series data c...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06N3/084G06Q10/04G06Q50/06G06N3/048
Inventor 王扬杨青刘杰岳顺民亢鸣哲王旭强邓君怡
Owner STATE GRID TIANJIN ELECTRIC POWER
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