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Visualized calculation of regional power grid power data based on multiple linear regression equation

Kun Chen
Kun Chen
, 
Xinhua Wang
Xinhua Wang
, 
Daniyal M. Alghazzawi
Daniyal M. Alghazzawi
 and 
Yanfeng Wang
Yanfeng Wang
   | Dec 15, 2021
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Published Online: Dec 15, 2021
Page range: 93 - 102
Received: Jun 16, 2021
Accepted: Sep 24, 2021
DOI: https://doi.org/10.2478/amns.2021.1.00054
Keywords
© 2021 Kun Chen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Equivalent circuit diagram of an ideal DC model of a photovoltaic module.DC, direct current.
Equivalent circuit diagram of an ideal DC model of a photovoltaic module.DC, direct current.

Fig. 2

The relationship between the output power of the module and the amount of radiation.PV, photovoltaic system.
The relationship between the output power of the module and the amount of radiation.PV, photovoltaic system.

Fig. 3

Test system.AC, alternating current; DC, direct current; PV, photovoltaic system; Vdc
Test system.AC, alternating current; DC, direct current; PV, photovoltaic system; Vdc

Fig. 4

Comparison of three types of weather prediction residuals.
Comparison of three types of weather prediction residuals.

Regression statistics

Weather type Sunny (I) Cloudy (II) Partly cloudy/overcast to cloudy (III) Uncategorized (IV)
R 0.997415 0.99715 0.99652 0.99667
R2 0.994838 0.99431 0.99314 0.99336
R-adjusted 0.98404 0.98545 0.98373 0.99026
Standard error, % 10.61 9.11 9.99 10.34
F-value 3051.26 3379.02 2,652.47 8,301.23
Significance value 0 0 0 0
Observation point 101 122 117 340

The output results of the coefficients of each equation in the regression calculation

Weather type N1′ \mathop N\nolimits_1^{'} N2′ \mathop N\nolimits_2^{'} N3′ \mathop N\nolimits_3^{'}
N4′ N5′ N6′ \mathop N\nolimits_4^{'} \,\mathop N\nolimits_5^{'} \,\mathop N\nolimits_6^{'} Sunny (I) X −1.15 0.55 −0.08 0.01 0.00 1.49 × 10−4
Standard deviation, % 24.14 113.67 16.85 2.57 0.52 0.03
T −0.48 0.48 −0.49 0.52 −0.53 0.53
Cloudy (II) X −0.80 0.40 −0.07 0.01 0.00 1.5 × 10−4
Standard deviation, % 63.56 31.45 5.05 0.94 0.20 0.01
T −1.26 1.29 −1.33 1.42 −1.43 1.44
Partly cloudy/overcast to cloudy (III) X −0.68 0.34 −0.06 0.01 0.00 1.3×10−5
Standard deviation, % 53.43 26.59 4.31 0.82 0.17 0.01
T −1.28 1.29 −1.32 1.38 −1.38 1.39
Unclassified (IV) X −0.47 0.24 −0.04 0.01 0.00 8.6 × 10−5
Standard deviation, % 34.72 17.13 2.73 0.50 0.10 5.6 × 10−3
T −1.35 1.38 −1.43 1.51 −1.52 1.53

Kyocera KC130GH-2P polysilicon module parameters

Component Pmax, W Um, V Im, A Uoc, V Isc, A Area, m2
Polysilicon 130 17.6 7.39 21.9 8.02 0.81

Analysis of variance

Weather type Regression analysis Residual Sum
Sunny (I) Df 6 95 101
SS 205.93 1.068 206.998
MS 34.32 0.0112 –
Cloudy (II) Df 6 116 122
SS 166.97 0.955 167.9272
MS 27.828 0.0082 –
Partly cloudy/overcast to cloudy (III) Df 6 110 116
SS 160.17 1.107 161.2801
MS 26.69 0.0101 –
Unclassified (IV) Df 6 333 339
SS 532.81 3.562 536.38
MS 88.8 0.0106 –
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Language:
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Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics
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