Probabilistic Forecasting of Wind Power Generation Using Online LASSO VAR and EGARCH Model

doi:10.16183/j.cnki.jsjtu.2021.377

Abstract

Abstract:

Wind power generation has uncertainty due to the high fluctuation of wind speed. In traditional wind power prediction models, the uncertainty is measured by normal distribution with zero mean and constant variance. However, the variance may vary with time, which means the variance has heteroscedasticity. To improve the prediction accuracy, this paper proposes a new integrated probabilistic wind power prediction model for wind farm considering heteroscedasticity based on online least absolute shrinkage and selection operator and vector autoregression (LASSO VAR) and the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model. First, online LASSO VAR is used to forecast power output. Then, heteroscedasticity of residuals is validated by autoregressive conditional heteroskedasticity test. Considering heteroscedasticity, the news impact curve and dynamic significance line verify that positive and negative residuals affect future volatility asymmetrically. Thus, the EGARCH model is used to forecast the residuals to obtain the conditional variance of point prediction results. Finally, the probabilistic result of total power output is obtained by summing the power output of turbines in the wind farm considering the correlation of the active wind power of wind turbines. This method is applied to forecast the power output of a wind farm in East China and is proved effective in improving the prediction accuracy.

Key words: online least absolute shrinkage and selection operator and vector autoregression (LASSO VAR), heteroscedasticity, exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, probabilistic forecasting

CLC Number:

TM614

WANG Peng, LI Yanting, ZHANG Yu. Probabilistic Forecasting of Wind Power Generation Using Online LASSO VAR and EGARCH Model[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 845-858.

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模型	RMSE	MAE	SMAPE
在线LASSO VAR(4)	397.08	272.03	55.65
在线LASSO VAR(3)	397.24	272.27	53.00
在线LASSO VAR(2)	406.98	280.50	54.16
在线LASSO VAR(1)	406.80	280.10	54.04
Batch LASSO VAR(4)	399.05	273.76	53.07
Batch LASSO VAR(3)	399.31	274.03	53.11
Batch LASSO VAR(2)	399.64	274.17	53.16
Batch LASSO VAR(1)	408.54	279.34	53.99
在线AR(4)	420.52	287.19	55.05

模型	RMSE	MAE	SMAPE
LASSO VAR(3)	399.31	274.03	53.11
ENET VAR(3)	399.03	278.56	53.90
Hierarchical Sparse VAR(3)	418.82	295.31	56.55
气象预测+功率曲线	449.6	308.56	54.22
添加气象数据的LASSO VAR(3)	407.1	280.4	54.12

模型	中位数预测 RMSE	K_t	CRPS
EGARCH (1, 1)	8019.4	-40401.8	4057.5
GARCH (1, 1)	8019.4	-40532.8	4070.8
BMA_48	8421.7	-43291.0	4389.9
BMA_24	8936.9	-45535.7	4640.5
KDE	32461.5	-158444	11345.67
QR	14918.7	-70989.3	7201.1
QRF	9182.3	-45429.4	4594.0