Original scientific paper

https://doi.org/10.17559/TV-20231019001038

A Wind Power Forecasting Model Incorporating Recursive Bayesian Filtering State Estimation and Time-Series Data Mining

Peng Liu ; School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China *
Tieyan Zhang ; School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Furui Tian ; State Grid Zhejiang Electric Power Company, LTD. Zhuji Power Supply Company, Zhuji311800, China
Yun Teng ; School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Chuang Gu ; Datang Heilongjiang Power Generation Co., Ltd. Harbin No.1 Thermal Power Plant, Harbin 150000, China

* Corresponding author.

Abstract

To enhance the precision of wind power forecasting and the integration of renewable energy, a wind power prediction model, synthesising recursive Bayesian filtering state estimation with time-series data mining, was developed. Initially, the Autoregressive Integrated Moving Average Model (ARIMA)-Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model was utilised for mining historical wind power data and establishing a model. Subsequently, the double-parameter t-distribution was employed to fit the prior estimation error and observation error, which integrated observational information with prior estimates through a sophisticated recursive Bayesian filtering approach, culminating in the formulation of a robust predictive model. Validation of this model was conducted using a diverse dataset, encompassing wind farms with varying capacities and distinct time intervals. Simulation outcomes reveal that this model's forecasting accuracy markedly surpasses that of conventional methodologies. Notably, an enhanced predictive precision is observed in wind farms with larger capacities, particularly when shorter intervals of observational data are employed. This model demonstrates significant potential for advancing the accuracy and efficiency of wind power forecasting, a critical element in the optimization of renewable energy utilization.

Keywords

data mining; information fusion; probability distribution fitting; recursive bayesian filtering; wind power forecasting

Hrčak ID:

320377

URI

https://hrcak.srce.hr/320377

Publication date:

31.8.2024.

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