Integrated Correction Method of Multi-point Numerical Weather Prediction Wind Speed and Irradiance

doi:10.12096/j.2096-4528.pgt.22005

Abstract

Abstract:

Numerical weather prediction (NWP) correction is one of the key technologies to improve the accuracy of wind and solar power forecasting. However, there are few researches on the correction of NWP irradiance. At the same time, most of the existing NWP wind speed correction methods only consider a single location, ignoring the spatio-temporal coupling of wind speed, which affects the correction effect. Aiming at this problem, this paper proposed an integrated correction method for multi-point NWP wind speed and irradiance considering the spatial and temporal correlation of regional wind and solar resources. Taking the measured wind speed and irradiance data of multiple wind farms and photovoltaic power plants in the region as the learning objective of the correction model, an integrated NWP correction model based on attentional neural network was established to simultaneously correct multiple station-level NWP data with certain correlation. The proposed method was verified by the NWP data and historical wind speed / irradiance data of 8 wind farms and 7 photovoltaic power plants in a region. The calculation results show that compared with the traditional single-point NWP correction method, the proposed method can effectively improve the accuracy of NWP.

Key words: wind farm, photovoltaic power plant, spatial and temporal correlation, numerical weather prediction (NWP), power forecasting, attentional neural network

CLC Number:

TK 81

Yongrui ZHANG, Jie YAN, Aimei LIN, Shuang HAN, Yongqian LIU. Integrated Correction Method of Multi-point Numerical Weather Prediction Wind Speed and Irradiance[J]. Power Generation Technology, 2022, 43(2): 278-286.

Figures/Tables 10

References 31

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网络层	神经元个数
全连接层1	32
全连接层2	64
全连接层3	32

网络层	神经元个数
全连接层1	32
全连接层2	64
全连接层3	32

电站	原始误差	单点修正误差	集中式修正误差
电站	原始误差	单点修正误差	SVM	BPNN	TCN	本文模型
SP1	171.37	85.99	88.67	77.49	79.02	76.26
SP2	226.33	186.00	132.13	135.24	144.72	125.72
SP3	231.55	204.50	110.20	108.72	117.97	106.67
SP4	115.39	100.65	112.91	101.39	110.24	96.49
SP5	129.48	129.55	131.84	131.76	138.72	128.51
SP6	132.59	79.20	92.15	80.54	76.04	78.41
SP7	116.03	105.22	115.76	108.65	110.45	104.75

电站	原始误差	单点修正误差	集中式修正误差
电站	原始误差	单点修正误差	SVM	BPNN	TCN	本文模型
SP1	171.37	85.99	88.67	77.49	79.02	76.26
SP2	226.33	186.00	132.13	135.24	144.72	125.72
SP3	231.55	204.50	110.20	108.72	117.97	106.67
SP4	115.39	100.65	112.91	101.39	110.24	96.49
SP5	129.48	129.55	131.84	131.76	138.72	128.51
SP6	132.59	79.20	92.15	80.54	76.04	78.41
SP7	116.03	105.22	115.76	108.65	110.45	104.75

电站	原始误差	单点修正误差	集中式修正误差
电站	原始误差	单点修正误差	SVM	BPNN	TCN	本文模型
SP1	87.16	43.58	52.02	46.28	43.36	40.40
SP2	116.17	100.36	74.88	70.66	76.62	61.23
SP3	110.71	148.42	57.25	58.22	59.81	53.11
SP4	56.09	47.10	66.44	52.56	51.62	45.28
SP5	60.46	65.21	71.60	65.42	69.88	61.19
SP6	67.56	36.00	54.46	39.64	40.99	35.54
SP7	57.88	46.95	63.63	57.10	51.51	48.03