Short-term power load prediction method based on KM-APSO-SVM (K-medoids-Adaptive Particle Swarm Optimization-Support Vector Machine) model

Abstract

The invention discloses a short-term power load prediction method based on a KM-APSO-SVM (K-medoids-Adaptive Particle Swarm Optimization-Support Vector Machine) model. The short-term power load prediction method comprises the following steps that: (1) on the basis of big data, carrying out the analysis of a power grid daily load change rule: collecting the data information of prediction site environment, utilizing grey relational analysis to analyze a relationship between each meteorological factor and a load, and laying a foundation for establishing a load prediction model; (2) applying a K-medoids clustering algorithm to carry out clustering analysis on a sample: arranging collected data to form a clustering sample, setting a classification number, selecting a relevant factor to form a sample feature vector, applying the K-medoids clustering algorithm to carry out the clustering analysis on the sample, and mapping to a specific zone through nondimensionalizign processing to form a clustering result; and (3) applying an APSO-SVM prediction model to carry out load prediction: carrying out accumulation preprocessing on the collected data to obtain a training sample, inputting the data of the clustering sample into the SVM to be trained, using the APSO to optimize SVM parameters, establishing a prediction model, and carrying out accumulation reduction on an obtained prediction result.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a short-term power load forecasting method based on the KM-APSO-SVM model. Background technique [0002] Today, with the rapid development of the electric power industry, the development level of the national economy is closely related to the pros and cons of the power load forecasting technology. Accurate power load forecasting is conducive to power grid enterprises to formulate scientific and reasonable power transmission and deployment plans, effectively avoid risks, and ensure the safety and reliability of power supply. Short-term power load forecasting is based on historical load data, considering the influence of weather, holidays, economic and other factors, mastering the fluctuation law of load and its internal relationship with various factors, selecting appropriate forecasting technology and using mathematical methods to infer the future hours or is the ...

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The neural network obtains prediction results through self-learning of historical data, but the determination of the number of layers and the nu...

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