• No results found

Distribution analysis of electricity measurement

N/A
N/A
Info
Download
Protected

Academic year: 2022

Share "Distribution analysis of electricity measurement"

Copied!
118
0
0

Loading.... (view fulltext now)

Download now ( 118 Page )

Full text

(1)

Distribution analysis of electricity measurement

Frequency of data saving: 1 s

1) Value: U3h,2[V]

Best interval for averaging (based on seconds): 42,523±3,631 s Best interval for averaging (based on file): 42,523±3,631 s

Legend of histogram Proportion legend

(2)

Comparison of proportion results (Value: U3h,2[V])

Comparison of distributions

(3)

A) Normal Distribution (Value: U3h,2[V])

B) LogNormal Distribution (Value: U3h,2[V])

(4)

C) Cauchy Distribution (Value: U3h,2[V])

(5)

2) Value: U3h,4[V]

Best interval for averaging (based on seconds): 21,169±1,492 s Best interval for averaging (based on file): 21,169±1,492 s

Legend of histogram Proportion legend

(6)

Comparison of proportion results (Value: U3h,4[V])

Comparison of distributions

(7)

A) Normal Distribution (Value: U3h,4[V])

B) LogNormal Distribution (Value: U3h,4[V])

(8)

C) Cauchy Distribution (Value: U3h,4[V])

(9)

3) Value: U3h,6[V]

Best interval for averaging (based on seconds): 11,87±0,406 s Best interval for averaging (based on file): 11,87±0,406 s

Legend of histogram Proportion legend

(10)

Comparison of proportion results (Value: U3h,6[V])

Comparison of distributions

(11)

A) Normal Distribution (Value: U3h,6[V])

B) LogNormal Distribution (Value: U3h,6[V])

(12)

C) Cauchy Distribution (Value: U3h,6[V])

(13)

4) Value: U3h,8[V]

Best interval for averaging (based on seconds): 39,424±3,593 s Best interval for averaging (based on file): 39,424±3,593 s

Legend of histogram Proportion legend

(14)

Comparison of proportion results (Value: U3h,8[V])

Comparison of distributions

(15)

A) Normal Distribution (Value: U3h,8[V])

B) LogNormal Distribution (Value: U3h,8[V])

(16)

C) Cauchy Distribution (Value: U3h,8[V])

(17)

5) Value: U3h,10[V]

Best interval for averaging (based on seconds): 43,134±2,926 s Best interval for averaging (based on file): 43,134±2,926 s

Legend of histogram Proportion legend

(18)

Comparison of proportion results (Value: U3h,10[V])

Comparison of distributions

(19)

A) Normal Distribution (Value: U3h,10[V])

B) LogNormal Distribution (Value: U3h,10[V])

(20)

C) Cauchy Distribution (Value: U3h,10[V])

(21)

6) Value: U3h,12[V]

Best interval for averaging (based on seconds): 43,532±4,939 s Best interval for averaging (based on file): 43,532±4,939 s

Legend of histogram Proportion legend

(22)

Comparison of proportion results (Value: U3h,12[V])

Comparison of distributions

(23)

A) Normal Distribution (Value: U3h,12[V])

B) LogNormal Distribution (Value: U3h,12[V])

(24)

C) Cauchy Distribution (Value: U3h,12[V])

(25)

7) Value: U3h,14[V]

Best interval for averaging (based on seconds): 39,48±4,609 s Best interval for averaging (based on file): 39,48±4,609 s

Legend of histogram Proportion legend

(26)

Comparison of proportion results (Value: U3h,14[V])

Comparison of distributions

(27)

A) Normal Distribution (Value: U3h,14[V])

B) LogNormal Distribution (Value: U3h,14[V])

(28)

C) Cauchy Distribution (Value: U3h,14[V])

(29)

8) Value: U3h,16[V]

Best interval for averaging (based on seconds): 31,145±4,802 s Best interval for averaging (based on file): 31,145±4,802 s

Legend of histogram Proportion legend

(30)

Comparison of proportion results (Value: U3h,16[V])

Comparison of distributions

(31)

A) Normal Distribution (Value: U3h,16[V])

B) LogNormal Distribution (Value: U3h,16[V])

(32)

C) Cauchy Distribution (Value: U3h,16[V])

(33)

9) Value: U3h,18[V]

Best interval for averaging (based on seconds): 25,05±3,226 s Best interval for averaging (based on file): 25,05±3,226 s

Legend of histogram Proportion legend

(34)

Comparison of proportion results (Value: U3h,18[V])

Comparison of distributions

(35)

A) Normal Distribution (Value: U3h,18[V])

B) LogNormal Distribution (Value: U3h,18[V])

(36)

C) Cauchy Distribution (Value: U3h,18[V])

(37)

10) Value: U3h,20[V]

Best interval for averaging (based on seconds): 23,815±2,491 s Best interval for averaging (based on file): 23,815±2,491 s

Legend of histogram Proportion legend

(38)

Comparison of proportion results (Value: U3h,20[V])

Comparison of distributions

(39)

A) Normal Distribution (Value: U3h,20[V])

B) LogNormal Distribution (Value: U3h,20[V])

(40)

C) Cauchy Distribution (Value: U3h,20[V])

(41)

Comparison of results:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(42)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(43)

Comparison of graphs between values:

1) Normal Distribution:

A) Proportion_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

(44)

U3h,10[V] U3h,12[V]

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(45)

B) Shapiro-Wilk P value_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(46)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(47)

C) Shapiro-Wilk Statistic_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(48)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(49)

D) KS P value_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(50)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(51)

E) KS Statistic_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(52)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(53)

F) STD_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(54)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(55)

G) Entropy_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(56)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(57)

H) Kurtosis_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(58)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(59)

I) Mean_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(60)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(61)

J) Median_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(62)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(63)

K) Mode_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(64)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(65)

L) Variance_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(66)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(67)

M) Quartiles Lenght_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(68)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(69)

N) Quartiles Max_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(70)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(71)

O) Quartiles Min_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(72)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(73)

2) LogNormal Distribution:

A) Proportion_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

(74)

U3h,10[V] U3h,12[V]

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(75)

B) P value_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(76)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(77)

C) Entropy_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(78)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(79)

D) Mean_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(80)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(81)

E) Median_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(82)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(83)

F) Mode_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(84)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(85)

G) Variance_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(86)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(87)

H) Quartiles Lenght_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(88)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(89)

I) Quartiles Max_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(90)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(91)

J) Quartiles Min_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(92)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(93)

K) Shape_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(94)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(95)

L) Shape2_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(96)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(97)

M) Location_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(98)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(99)

3) Cauchy Distribution:

A) Proportion_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

(100)

U3h,10[V] U3h,12[V]

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(101)

B) P value_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(102)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(103)

C) Entropy_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(104)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(105)

D) Median_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(106)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(107)

E) Mode_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(108)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(109)

F) Quartiles Lenght_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(110)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(111)

G) Quartiles Max_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(112)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(113)

H) Quartiles Min_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(114)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(115)

I) Scale_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(116)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

(117)

J) Location_RANDOM:

U3h,2[V] U3h,4[V]

U3h,6[V] U3h,8[V]

U3h,10[V] U3h,12[V]

(118)

U3h,14[V] U3h,16[V]

U3h,18[V] U3h,20[V]

References

Download now ( PDF - 118 Page - 50.12 MB )

Related documents

Digitaliserad läsning: En studie av hur pojkar och flickor uppfattar läsning och navigering på dator

Valet att använda just detta test baserade vi dels på det faktum att frågorna avsåg testa olika former av läsning, vilket skulle kunna ge eleverna en bredare och mer fördjupad

A Study in the Computational Complexity of Temporal Reasoning

This is a powerful method which allows us to automatically identify, for instance, all the tractable sets of relations for the point algebras (with disjunctions) for totally ordered

Third workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR)

By semantic annotations we refer to linguistic annotations (such as named entities, semantic classes or roles, etc.) as well as user annotations (such as microformats, RDF,

ON THE JACKKNIFE AVERAGING OF GENERALIZED LINEAR MODELS

The plots suggest that some averaging estimators generate smaller risk than single model methods, seeing as both regularization path averaging and LASSO-GLM hybrid averaging

Distribution analysis of electricity measurement

Comparison of distributions.. A) Normal Distribution (Value: avg.U2[V]). B) LogNormal Distribution (Value: avg.U2[V]).. C) Cauchy Distribution

:/;!,!523!

The mean difference is significant at the 0.05 level... The mean difference is significant at the

A Novel two-step Method for CT Reconstruction

the three-step Hilbert method, the projection data is multiplied with the ori- entation vector before back-projection and the Hilbert transform is computed on the divergence

Signal Averaging for Digitizer ADQ214

If there is a trigger event at cycle 7, the state machine will send a signal to pre-trigger buffer to read out the first sample, send appropriate control signals to the