A Novel Weather Classification Method and Its Application in Photovoltaic Power Prediction

doi:10.16183/j.cnki.jsjtu.2021.264

Abstract

Abstract:

To improve the accuracy of photovoltaic (PV) power prediction, this paper proposes a novel weather classification method. First, it distinguishs the clear days and cloudy days according to the total cloud cover. Then, it further classifies the cloudy days into three subtypes to investigate whether the sun is obscured by clouds. This method can effectively identify the characteristics of key meteorological environmental factors that affect PV output and form a new classification index sky condition factor (SCF) by weighted summation. This method has clear physical meanings, good discrimination, and easy quantification. The reasonable classification of weather types can eliminate the coupling relationship between many meteorological environmental factors and reduce the dimension of input variables, which makes it easy for statistical modeling. Based on the theoretical and the statistical approachs respectively, the modeling and verification are conducted and the results show that the method can effectively improve the accuracy of PV power prediction.

Key words: photovoltaic (PV) power prediction, weather type classification, meteorological environmental factors, physical approach, statistic approach

CLC Number:

TM615
P49

LI Fen, ZHOU Erchang, SUN Gaiping, BAI Yongqing, TONG Li, LIU Bangyin, ZHAO Jinbin. A Novel Weather Classification Method and Its Application in Photovoltaic Power Prediction[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1510-1519.

Figures/Tables 16

Fig.1

Tab.1

Tab.2

Fig.2

Fig.3

Fig.4

Tab.3

Nonlinear regression models

模型名称	表达式	RSS	R²
Metcherlich	$y = b 1 + b 2 exp (- b 3 P)$	3.140	0.983
Johnson-Schumacher	$y = b 1 exp (- b 2 / (P + b 3))$	1.376	0.992
Morgen-Mercer-Florin	$y = (b 1 b 2 + b 3 P b 4) / (b 2 + P b 4)$	1.372	0.992
Peal-Reed	$y = b 1 / {1 + b 2 exp [- (b 3 P + b 4 P 2 + b 5 P 3)]}$	1.807	0.990
Richards	$y=b_{1}/\{[1+b_{3}exp(-b_{2}P)]^{1/b_{4}}\}$	1.470	0.992

Tab.3

Tab.4

Fig.5

Tab.5

Tab.6

Fig.6

Tab.7

Fig.7

Tab.8

Tab.9

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变量	R
I	0.868
S_p	0.672
k'_T	0.657
B_d	0.612
RH	-0.442
V	0.378
T	0.262
C	-0.254
W	0.150
RA	-0.123
p	-0.007

天气类型	个案数目	μ_SCF取值范围
2	1714	ξ₁<μ_SCF≤ξ₂
3	1477	ξ₂<μ_SCF≤ξ₃
4	2468	ξ₃<μ_SCF≤ξ₄

模型	天气类型1		天气类型2		天气类型3		天气类型4
模型	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%
Perez	24.87	36.32	23.32	29.95	38.28	52.57	52.68	88.86
Hay	27.73	39.95	26.23	33.62	38.20	52.67	52.51	88.71
Reindl	27.64	39.88	26.03	33.49	38.16	52.55	52.38	88.59
Klucher	27.55	39.24	25.90	33.06	43.16	55.78	59.49	89.50
Liu & Jordan	25.05	36.48	23.54	30.07	37.14	51.08	20.72	32.91

变量名	与P_ac的相关系数
变量名	天气类型1	天气类型2	天气类型3	天气类型4
I	0.884	0.736	0.748	0.690
I_b	0.820	0.668	0.523	0.194
I_d	0.604	0.425	0.664	0.690
B_d	0.598	0.311	-0.045	-0.092
S_p	0.581	0.368	0.258	0.112
k'_T	0.536	0.502	0.331	0.007
V	0.385	0.152	0.233	0.200
W	0.341	0.166	0.080	-0.031
RH	-0.247	-0.283	-0.307	-0.348
T	0.119	0.087	0.249	0.308
C	-0.052	0.046	-0.008	-0.072
R	-0.041	0.007	-0.126	-0.119

模型	天气类型1		天气类型2		天气类型3		天气类型4
模型	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%	MAPE/%	NRMSE/%
LR	20.06	25.11	18.87	22.30	33.43	58.09	47.50	70.39
GPR	13.18	19.73	15.78	18.47	32.94	58.93	34.73	61.58
SVR	14.52	20.40	14.30	16.94	33.87	58.33	31.56	54.50
Adaboost	16.61	24.15	19.38	23.42	31.02	52.56	42.94	66.29