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image of Short-term Load Forecasting Method Based on VNCMD-TiDE Model

Abstract

Background

Extreme weather conditions exert a considerable influence on power load, leading to increasingly erratic fluctuations. Consequently, the dependable and precise forecasting of power load assumes paramount importance in power system planning.

Objective

Given the inadequacy of traditional forecasting approaches in handling long-term series load forecasting, this paper introduces a short-term power load forecasting model rooted in VNCMD-TiDE, aiming to enhance forecasting precision.

Method

Initially, the XGBoost algorithm is employed to perform nonlinear coupling analysis between load and meteorological data, identifying crucial features. Following this, the VNCMD method is utilized to handle the nonlinear and non-stationary load data, decomposing it into multiple components with distinct frequencies. Building upon this decomposition, a TiDE-Bayesian model is constructed, wherein the decomposed components serve as inputs for prediction. Simultaneously, Bayesian optimization is leveraged to fine-tune hyperparameters. Ultimately, the prediction outcomes of each component are amalgamated to derive the final prediction.

Results

The proposed model's performance is assessed through comparison with traditional machine learning models, such as LSTM. Achieving a noteworthy reduction in Root Mean Square Error (RMSE) by 4.14 underscores its exceptional predictive prowess.

Conclusion

Through the analysis of actual power load data in a specific location, the model proposed in this article demonstrates superior prediction accuracy, particularly evident during extreme weather conditions like snow and rain.

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2025-01-06
2025-06-17

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/content/journals/raeeng/10.2174/0123520965331085240926053147
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Short-term Load Forecasting Method Based on VNCMD-TiDE Model
Bentham Science Publishers ; https://doi.org/10.2174/0123520965331085240926053147
/content/journals/raeeng/10.2174/0123520965331085240926053147
/content/journals/raeeng/10.2174/0123520965331085240926053147
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  • Article Type:     Research Article
Keywords: XGBoost ; short-term forecast ; Load forecast ; VNCMD decomposition ; TiDE

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