Residential electricity consumption mid-term load prediction method under multistep electricity price mechanism

Abstract

The invention relates to a residential electricity consumption mid-term load prediction method under a multistep electricity price mechanism. At first, the residential electricity consumption data is acquired, the attributive characters of residential electricity consumption behaviors under the multistep electricity price mechanism are extracted, different electricity consumption behavior characters of the residents under the multistep electricity price mechanism can be identified through cluster analysis, and the users having same or similar electricity consumption behavior characters are clustered into the same user category; corresponding load prediction models are established for the user categories, and the predication is carried out; at the end, the predication results of the user categories are summarized. The multistep electricity price related indexes are innovatively introduced into a clustering model, and the mid-term load prediction can be better carried out by means of the accurate and comprehensive data provided by a smart electric meter. Compared with a conventional manner that the data acquisition is carried out every 15 minutes, the method provided performs the mid-term prediction with fewer data sampling points (data per day) under the premise of guaranteeing the precision.

Description

technical field [0001] The invention relates to the fields of load forecasting and data mining, and more specifically, relates to a mid-term load forecasting method for residents' electricity consumption under a ladder electricity price mechanism. Background technique [0002] Since the implementation of the tiered electricity price for residents, initial results have been achieved, and to a certain extent, residents' awareness of energy conservation has been enhanced, and some bad habits of electricity consumption have been changed. Under the traditional single low-price electricity price system, the difference in electricity consumption behavior (rather than electricity consumption) among user groups with different characteristics is not obvious. [0003] However, under the new tiered electricity price system, user groups with different characteristics (such as income, family structure, living habits, etc.) will have different responses to the tiered electricity price, and...

AI Technical Summary

Problems solved by technology

The traditional total quantity forecast establishes the average user behavior model, which cannot reveal the different behaviors of various customer groups, and ignores the differences in the electricity consumption behavior of different types of users

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