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. 2017 May 17;13:60–68. doi: 10.1016/j.dib.2017.05.017

Exploration of solar radiation data from three geo-political zones in Nigeria

Adebowale O Adejumo a,b, Esivue A Suleiman a, Hilary I Okagbue a,⁎
PMCID: PMC5447380  PMID: 28580405

Abstract

In this paper, readings of solar radiation received at three meteorological sites in Nigeria were analysed. Analysis of Variance (ANOVA) statistical test was carried out on the data set to observe the significant differences on radiations for each quarter of the specified years. The data were obtained in raw form from Nigerian Meteorological Agency (NIMET), Oshodi, Lagos. In order to get a clear description and visualization of the fluctuations of the radiation data, each year were considered independently, where it was discovered that for the 3rd quarter of each year, there is a great fall in the intensity of the solar radiation to as low as 73.27 (W/m2), 101.66 (W/m2), 158.51 (W/m2) for Ibadan, Port-Harcourt and Sokoto respectively. A detailed data description is available for the averages across months for each quarter. The data can provide insights on the health implications of exposure to solar radiation and the effect of solar radiation on climate change, food production, rainfall and flood patterns.

Keywords: ANOVA, Solar radiation, Tukey׳s Post Hoc, Port Harcourt, Sokoto, Ibadan

Specification Table

Subject area Environmental Science
More specific subject area Solar Radiation
Type of data Table and figure
How data was acquired Unprocessed secondary data
Data format Processed as Monthly Averages Across Quarters from 2011 to 2015 for Three Meteorological Sites
Experimental factors Data obtained from Nigerian Meteorological Agency (NIMET)
Experimental features Computational Analysis: Analysis of Variance (ANOVA) with Post Hoc Test and Correlation Analysis.
Data source location Ibadan, Port-Harcourt and Sokoto Meteorological Stations.
Data accessibility All the data are in this data article.
Software Microsoft Excel and Minitab 17 Statistical Software
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Value of the data

  • ●

    The energy sector of the economy can incorporate the data set and findings for the utilization of solar radiation received from the sites.

  • ●

    The vitality of these data set is widely recognised in the energy research community for forecasting minutely, hourly, daily and monthly solar radiations using time series tools which could also cater for volatility that exist in the data.

  • ●

    For educational purposes and environmental studies. See similar works [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33].

  • ●

    Findings from the data bring the awareness of the Nigerian government to the most suitable location for the establishment of both solar plants and research institutes to generate electricity.

  • ●

    The data can provide insights on the health implications of exposure to solar radiation.

1. Data

The raw data for this work were obtained from Nigerian Meteorological Agency (NIMET) Oshodi Lagos, as daily averages on solar radiation for three weather stations namely; Ibadan, Sokoto and Port-Harcourt. The readings were taken using the Gunn-Bellani Radiation Integrator measuring the radiations in millilitres (ml). However, for the sake of this research, the readings were converted to Watts per Sq. meters (1 ml to 13.153 W/m2) covering from 1st of January, 2011 to 31st of December, 2015 and further transformed into monthly-quarterly averages (Table 7) for the specified years using Microsoft Excel software.

Table 7.

Monthly-quarterly solar radiation for three sites from 2011 to 2015.

Year Month Quarter Ibadan Sokoto Port Harcourt
2011 Jan Q1 152.9544 216.6395 195.3404
2011 Feb Q1 167.1816 221.9066 173.1943
2011 Mar Q1 179.9389 244.9392 180.448
2011 Apr Q2 162.218 242.0993 182.9117
2011 May Q2 154.397 232.1683 160.7612
2011 Jun Q2 126.53 189.8827 131.3527
2011 Jul Q3 90.88166 168.1862 121.4725
2011 Aug Q3 91.89994 163.3494 114.3021
2011 Sep Q3 112.588 218.2928 134.7724
2011 Oct Q4 141.0745 231.2348 141.5836
2011 Nov Q4 166.8653 259.2857 155.8608
2011 Dec Q4 186.7699 220.9247 188.9337
2012 Jan Q1 153.8454 233.9503 169.7561
2012 Feb Q1 149.9874 236.8866 148.0371
2012 Mar Q1 171.4956 255.0796 157.5473
2012 Apr Q2 168.6628 254.1561 155.8032
2012 May Q2 140.2683 228.6467 175.9798
2012 Jun Q2 106.2308 198.5635 155.5539
2012 Jul Q3 98.30663 170.3925 123.297
2012 Aug Q3 84.72954 163.9434 132.4191
2012 Sep Q3 102.2412 237.7151 139.4198
2012 Oct Q4 148.6692 225.507 169.9258
2012 Nov Q4 149.4159 235.8298 164.1032
2012 Dec Q4 156.1366 252.3218 183.3756
2013 Jan Q1 143.9172 241.5025 190.2066
2013 Feb Q1 152.4786 263.7138 182.1664
2013 Mar Q1 170.1379 274.2572 190.2914
2013 Apr Q2 153.0987 246.2206 190.1896
2013 May Q2 152.318 266.2807 155.0758
2013 Jun Q2 122.4965 238.1973 118.5945
2013 Jul Q3 90.37251 204.8868 104.1193
2013 Aug Q3 91.51808 158.5125 104.4163
2013 Sep Q3 123.5487 220.8356 103.7318
2013 Oct Q4 147.5236 196.0405 160.1248
2013 Nov Q4 159.2367 215.5635 140.6912
2013 Dec Q4 161.1007 241.7571 180.2359
2014 Jan Q1 151.5119 260.3407 187.0669
2014 Feb Q1 165.4435 265.0291 185.4546
2014 Mar Q1 161.6947 283.3369 149.3904
2014 Apr Q2 177.256 273.8853 173.354
2014 May Q2 166.4042 245.7454 155.1183
2014 Jun Q2 135.6931 251.1309 121.3127
2014 Jul Q3 99.66434 213.1179 105.4346
2014 Aug Q3 73.27386 168.3135 109.7623
2014 Sep Q3 109.8259 233.287 103.2934
2014 Oct Q4 136.8316 265.9048 159.7854
2014 Nov Q4 153.5371 289.4494 132.2734
2014 Dec Q4 170.6895 273.8075 168.4408
2015 Jan Q1 179.5146 253.1279 188.5943
2015 Feb Q1 179.7237 308.7152 166.6179
2015 Mar Q1 175.399 269.7174 169.0348
2015 Apr Q2 177.2122 293.1761 157.4829
2015 May Q2 178.2418 274.8512 145.487
2015 Jun Q2 131.3527 256.4359 124.2063
2015 Jul Q3 106.962 227.5436 101.6585
2015 Aug Q3 105.1376 201.4077 112.5626
2015 Sep Q3 110.3959 218.6873 128.3276
2015 Oct Q4 132.8434 244.4301 156.8578
2015 Nov Q4 152.7041 250.2541 187.9975
2015 Dec Q4 172.3018 209.2145 231.9561
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Table 1a is the statistical summary of the quarterly averages for solar radiation from the 1st of January 2011 to 31st of December 2015. Meanwhile, it was observed that on an average, Sokoto receives the highest intensity of solar radiation followed by Port Harcourt and Ibadan.

Table 1a.

Summary for the quarterly average for the sites.

Variable N Mean S.E Mean Std. Dev. Minimum Q1 Median Q3 Maximum
Ibadan 60 142.24 3.84 29.76 73.27 115.07 151.91 166.75 186.77
Sokoto 60 235.01 4.40 34.08 158.51 217.05 237.96 258.57 308.72
Port H 60 153.29 3.85 29.85 101.66 129.08 156.36 179.17 231.96
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Furthermore, Table 1b shows that the data set from all sites exhibit negative kurtosis and skew, implying that the distributions are light-tailed and skewed to the left respectively.

Table 1b.

Summary for the quarterly average for the sites.

Variable Kurtosis Skewness
Ibadan −0.77 −0.60
Sokoto −0.02 −0.45
Port H −0.57 −0.05
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From the graphs (Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5), it was observed that Sokoto was top on the presented charts with an exception on December 2015. This validates that the closer the earth׳s surface is to the sun, the greater the radiations it receives, which is well applicable to the case of Sokoto ranking top among the other stations with a height of 309 m above sea level. Furthermore, the solar radiation received at Sokoto increases yearly within the 1st quarter. It has to be noted that the y-axis of the figures is the solar radiation reading for the zones measured in Watt per square meter.

Fig. 1.

Fig. 1

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Quarterly averages of solar radiation for the three sites in the year 2011.

Fig. 2.

Fig. 2

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Quarterly averages of solar radiation for the three sites in the year 2012.

Fig. 3.

Fig. 3

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Quarterly averages of solar radiation for the three sites in the year 2013.

Fig. 4.

Fig. 4

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Quarterly averages of solar radiation for the three sites in the year 2014.

Fig. 5.

Fig. 5

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Quarterly averages of solar radiation for the three sites in the year 2015.

2. Methods and materials

The summary of the location sites of the raw data are displayed in Table 2.

Table 2.

Location of the sites.

Sites Latitude Longitude Height (m)
Ibadan 07.22′ 03.59′ 224.01
Sokoto 12.55′ 05.12′ 309.0
Port Harcourt 05.01′ 06.57′ 247.0
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Linear correlation is traditionally used to roughly determine the relationship between two variables. Table 3 shows the correlation matrix between the three meteorological stations. Though independent, the correlations among each station are positive and that of Sokoto–Ibadan and Ibadan–Port Harcourt are highly positively correlated. It is either the solar radiation levels are increasing or decreasing at the three sites simultaneously.

Table 3.

3×3 Correlation matrix for the sites.

Sites Ibadan Sokoto Port Harcourt
Ibadan 1
Sokoto 0.741099 1
Port H 0.755118 0.426821562 1
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The ANOVA test carried out on the data set for all sites and the result was displayed in Table 4, Table 5, Table 6. The results showed significant differences in the means for solar radiation received quarterly at the stations independently.

Table 4.

Analysis of Variance (ANOVA) for Port Harcourt.

Source of variation D.F S.S M.S F-value P-value
Quarters 3 31672 10557.3 28.29 0.000
Error 56 20898 373.2
Total 59 52570
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Table 5.

Analysis of Variance (ANOVA) for Sokoto.

Source of variation D.F S.S M.S F-value P-value
Quarters 3 29161 9720.4 13.83 0.000
Error 56 39364 702.9
Total 59 68526
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Table 6.

Analysis of Variance (ANOVA) for Ibadan.

Source of variation D.F S.S M.S F-value P-value
Quarters 3 38059 12686.3 50.04 0.000
Error 56 14197 253.5
Total 59 52256
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The significant differences in the means as revealed from the ANOVA results led to further analysis using the Tukey׳s Simultaneous 95% Confidence Interval Post Hoc test. The aim is to detect the specific quarters where differences lie across the specified years.

The results revealed that for Port Harcourt, significant differences lie within all other quarters except for the 1st and 4th quarters and for the 2nd and 4th quarters yearly as seen in Fig. 6. Similarly, Fig. 7 shows that for Sokoto, the 1st and 3rd quarters, 3rd and 4th quarters and the 2nd and 3rd quarters as having significant differences. Lastly, Fig. 8 shows that for Ibadan, significance differences exists between the 1st and 3rd Quarters, 2nd and 3rd Quarters and the 3rd and 4th Quarters for these years. Minitab17 software was implemented for analysis on the solar radiation data, which produced the ANOVA results and the Post Hoc test for the stations (Table 7).

Fig. 6.

Fig. 6

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Tukey׳s Post Hoc test for mean-quarterly differences in Port Harcourt site from 2011 to 2015.

Fig. 7.

Fig. 7

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Tukey׳s Post Hoc test for mean-quarterly differences in Sokoto site from 2011 to 2015.

Fig. 8.

Fig. 8

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Tukey׳s Post Hoc test for mean-quarterly differences in Ibadan site from 2011 to 2015.

Acknowledgement

This work is sponsored by Centre for Research, Innovation and Discovery, Covenant University, Ota, Nigeria.

Footnotes

Transparency document

Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2017.05.017.

Transparency document. Supplementary material

Supplementary material

mmc1.pdf (137.1KB, pdf)

.

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Supplementary Materials

Supplementary material

mmc1.pdf (137.1KB, pdf)

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