Power utilization load prediction method based on adaptive hierarchical time sequence clustering

Abstract

The invention discloses a power utilization load prediction method based on adaptive hierarchical time sequence clustering. The method comprises the steps: 1), sequentially dividing load sequences on a window time segment according to the rise, reduction and leveling-off features of a power utilization load after quantification; 2), carrying out the agglomerative hierarchical clustering of the load sequence features through employing a hierarchical clustering method; 3), carrying out the prediction of the load through the hierarchical idea, enabling the latter load sequence in a prediction load sequence group to serve as a prediction load of a next moment, and enabling the predicted load sequence to serve as the current latest load sequence; 4), dynamically adjusting a quantification factor, a time window and a clustering parameters through a feedback method, and completing the power utilization load prediction. Therefore, the method is more precise in a supershort period, effectively reduces the storage price of original data in load prediction, and plays a support role in scientific and accurate power dispatching of an intelligent power grid.

Description

technical field [0001] The invention relates to the field of power consumption information processing of smart grids, in particular to a power load forecasting method based on self-adaptive hierarchical time series clustering. Background technique [0002] Under the condition of smart grid, various advanced metering devices (such as sensors and smart meters) are increasingly installed in the distribution network to monitor, control and predict the use of electric energy. Those collected transformers or power at different time intervals The user's daily consumption data constitutes the load curve of each monitoring point. These accurate and detailed electricity consumption information provide the basis for power distribution companies to obtain load patterns through specialized analysis. At present, the technology of load pattern extraction at home and abroad mainly uses various cluster analysis techniques to obtain typical load curves and load characteristics of power users...

AI Technical Summary

Problems solved by technology

However, the current clustering technology cannot satisfy the accurate clustering of the load on the time series, which makes the clustering technology have certain limitations for load prediction in a short period of time.

Currently, there is no load forecasting method based on hierarchical time series clustering with adaptive parameter adjustment

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