Multi-view Adaptive Probabilistic Load Forecasting Combing Bayesian Autoformer Network

ZHOU Shiqi; WANG Junfan; LAI Junsheng; YUAN Yujie; DONG Zhekang

doi:10.11999/JEIT240398

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ZHOU Shiqi, WANG Junfan, LAI Junsheng, YUAN Yujie, DONG Zhekang. Multi-view Adaptive Probabilistic Load Forecasting Combing Bayesian Autoformer Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240398

Citation:

ZHOU Shiqi, WANG Junfan, LAI Junsheng, YUAN Yujie, DONG Zhekang. Multi-view Adaptive Probabilistic Load Forecasting Combing Bayesian Autoformer Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240398

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Multi-view Adaptive Probabilistic Load Forecasting Combing Bayesian Autoformer Network

doi: 10.11999/JEIT240398

ZHOU Shiqi^{1, 2},
WANG Junfan^{1, 2},
LAI Junsheng³,
YUAN Yujie^{4
,
,},
DONG Zhekang^{1, 2}

1.
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Province Key Lab of Equipment Electronics, Hangzhou 310018, China
3.
Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK
4.
School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62206062), Yangtze River Delta Science and Technology Innovation Community Jointly Tackled Key Project (2023CSJGG1300), The Fundamental Research Funds for the Provincial University of Zhejiang (GK229909299001-06)

Received Date: 2024-05-21
Rev Recd Date: 2024-08-26

Available Online: 2024-08-30

Abstract

Abstract

Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
- Load forecasting,
- Probabilistic forecasting,
- Bayesian neural network,
- Autoformer

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References(24)

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