负荷预测 Resources

Showing items tagged with "负荷预测"

Application Background: This code is highly practical for load forecasting and related applications. It has been successfully implemented in wind power prediction projects. Beneficial for users seeking source code references, this solution is based on understandable logic with provided sample data - simply replace with your own datasets for immediate implementation. Key Technology: Primarily utilizing MATLAB's computational capabilities with standard programming constructs, the code generates graphical outputs for daily/monthly load forecasting. The prediction algorithm leverages historical power data patterns, employing time-series analysis techniques to model and project future load demands.

MATLAB 373 views Tagged
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High-quality research paper with accompanying source code. This study first reviews the application research status of load forecasting, summarizes the characteristics and influencing factors of load forecasting, categorizes common methods for short-term load forecasting, and analyzes the advantages and disadvantages of various methods. It then introduces the statistical learning theory as the theoretical foundation of Support Vector Machines (SVM) and explains SVM principles, deriving the SVM regression model. The paper employs a Least Squares Support Vector Machine (LSSVM) model, utilizing historical load data and meteorological data from Taizhou, Zhejiang Province to analyze various factors affecting predictions and summarize load variation patterns. The implementation includes preprocessing steps such as correcting "abnormal data" in historical load records and normalizing relevant factors for load forecasting. The study specifically addresses the significant impact of two key parameters in LSSVM models, which are currently determined empirically. The methodology incorporates parameter optimization using Particle Swarm Optimization (PSO) algorithm, where test set error serves as the criterion for parameter selection, demonstrating improved prediction accuracy through systematic parameter tuning.

MATLAB 207 views Tagged
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BP Neural Network applied to load forecasting and electricity price estimation, with detailed explanations of each component's function. Includes sample datasets and result visualization graphs that demonstrate practical implementation outcomes.

MATLAB 187 views Tagged
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