Medium-and-long time electric power load prediction method based on fuzzy clustering

Abstract

The invention discloses a medium-and-long-time electric power load prediction method based on a fuzzy cluster, comprising steps of determining a prediction amount and an influence factor, obtaining sample data of various influence factors in a certain time frame through observation, establishing a fuzzy similarity relation of the sample data, analyzing the uniqueness, the similarity and the affinity degree of various samples, performing incorporation, classification and screening on the approximation sample, establishing a new behavior factor (load influence factor after clustering ) which is relatively independent and low in correlation, analyzing and calculating the gray absolute correlation degree and the weight coefficient of the sample sequence of the various sample sequences and the prediction quantity sequence (main behavior), fitting a data prediction value as an independent variable according to the sample clustering result and establishing a prediction model of a prediction quantity. The medium-and-long term electric power load prediction method based on fuzzy clustering is scientific, reasonable, easy to implement, accurate in prediction, strong in adaptability and applicable to the medium and long term power load prediction.

Description

technical field [0001] The invention relates to a medium and long-term power load forecasting method based on fuzzy clustering, which is suitable for forecasting the annual maximum load and annual power consumption of a power grid. Background technique [0002] The load curve reflects the user's electricity consumption characteristics and rules. Through the load change trend, the power system operation plan, power supply equipment plan, equipment maintenance plan, etc. are arranged. The medium and long-term load change trend is the basis of power grid planning, and power grid planning is Relying on power grid construction. Therefore, how to accurately carry out load forecasting has become a prerequisite for improving the rationality of power grid operation and the quality of planning. [0003] The power load is affected by many factors, with strong uncertainty and randomness, and there is a certain correlation between them. When using traditional methods for power load fore...

AI Technical Summary

Problems solved by technology

Traditional forecasting methods mostly model and analyze load-related indirect factors such as "load-related economic data" or the power load data sequence itself, and effectively use some implicit information of load-related economic data or sequence self-response. not comprehensive enough

Images