基于改进灰色模型的电力用能行为分析与预测OA

[Objective]Traditional grey models are widely applied to short-term load prediction due to their sound adaptability to small-sample and information-poor data.However,when handling complex electricity consumption data featuring both exponential growth and linear trends,they suffer from inherent limitations,such as insufficient prediction accuracy,sensitivity to data noise,and weak generalization capability,thus making it difficult to meet the demands of modern refined power management.Given the shortcomings of traditional grey models,a comprehensively improved prediction framework was proposed to significantly enhance the accuracy and practicality of electricity consumption behavior prediction,thereby providing more reliable data support for intelligent management of power systems.[Methods]In the data preprocessing stage,the standard deviation method was adopted to identify and remove outliers,while the linear interpolation method was applied to fill missing values in electricity consumption data with dense collection cycles.During the stage of analyzing user consumption behavior,the K-means clustering algorithm was employed to process load curves,and the elbow method was utilized to determine the optimal number of clusters,identifying user groups with similar consumption patterns.In the stage of prediction model building,an improved grey model was proposed to integrate the traditional grey model with a linear regression model for building a fused grey-linear regression model.In the fused model,sequences were generated via accumulation,and fitting was conducted by employing the combined equation,with the parameters estimated via sequence transformation and the least squares method.Meanwhile,the fused model was utilized to predict the residual sequence,and the Fourier transform was introduced for spectral analysis and noise reduction.A Fourier basis matrix was constructed,and related coefficients were solved by adopting the least squares method to correct the original predicted values.[Results]Validation based on the actual data from 205 users in a specific region demonstrates that the improved model successfully identifies four typical electricity consumption patterns by clustering analysis.The proposed improved grey model was compared with the three baseline models of the traditional grey model,the grey model+linear model,and the grey model+residual correction model.The results show that the improved model exhibits significantly lower mean absolute error(MAE)and mean absolute percentage error(MAPE)than the other three models across all user categories and prediction time points.Its advantage is particularly pronounced during the initial prediction periods,indicating that the model is more suitable for short-term load prediction.[Conclusions]Clustering,linear compensation,and Fourier-based residual correction are integrated in the improved grey model.The classification foundation is provided for refined user management by K-means clustering.The traditional model's lack of linear fitting capability is effectively compensated for by linear regression,while noise and systematic errors are significantly reduced by Fourier-based residual correction.A substantial improvement in the model's accuracy and generalization capability is led to by the combination of the three elements.The model demonstrates excellent performance in short-term load prediction,holding practical significance in real-time electric power dispatch,demand response,economical energy usage,and cost reduction.The improved model is mainly applicable to short-term electric power load prediction,and future research will explore the integration with machine learning or the introduction of more factors to enhance its ability for medium-to-long-term electric power load prediction.