Subentry load prediction method for fine-grained electricity consumption behaviors of resident users

Abstract

The invention discloses a subentry load prediction method for fine-grained electricity consumption behaviors of resident users. The method comprises the steps of obtaining historical sample data of subentry electricity consumption of the resident users in a community; constructing a training sample set and a prediction sample set of a prediction model; taking the date corresponding to each subentry electric quantity in the training sample, holiday or workday, each temperature, peak valleys and each corresponding subentry electric quantity as the input of an AdaBoost iterative algorithm, and training a model; taking the date, holiday or workday, temperature and peak valley corresponding to each subentry electric quantity in the prediction sample as the input of an AdaBoost iterative algorithm to obtain a corresponding output result; and performing influence factor addition processing on the output result to obtain each sub-item load data of the resident user in a certain day in the future. According to the method, the fine-grained subentry load data of urban residents can be scientifically predicted, and the technical problems that an accurate model is difficult to establish to predict due to small subentry power load data of residential areas and complexity and variability of influence factors and the like are solved.

Description

technical field [0001] The invention belongs to the technical field of electric power system load forecasting, and relates to a sub-item load forecasting method for fine-grained electricity consumption behavior of resident users, and in particular to a load forecasting method for the sub-item electric quantity of residents based on influencing factors. Background technique [0002] With the rapid development of the global economy, the power industry, especially the development of the smart grid, has put forward higher requirements for all departments of the power system from a monopoly operation model to a competitive relationship. Only by conducting comprehensive and detailed research on the data related to load forecasting, formulating efficient and economical power generation plans, and rationally arranging unit output, the power sector can continuously provide users with safe and reliable power, meet the needs of each user, and ensure the safety and stability of the power...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the technical problem that the prior art cannot accurately predict the sub-item load, the present invention provides a sub-item load forecasting method for the fine-grained electricity consumption behavior of residential users, which can scientifically predict the fine-grained sub-item load data of urban residential quarters

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