Now process the Clean Energy Regulator dataā€¦..

Noosa LGA - Clean Energy Regulator data

As at June 30, 2022

The number of solar installations was 13,974 with total capacity of 76,016 kW.

This represents an increase in capacity of 10,071 kW over the last 12 months, giving an average monthly increase of 839 kW per month.

The corresponding increase in the number of solar installations in the last 12 months was 1,077, giving an average monthly increase of 90 new installations per month.

The increase in capacity over the last 12 months was 15.3 %

The increase in the number if installations over the last 12 months was 8.4 %

Assumption: The percentage of data for postcode 4573 that is attributed to the Noosa Local Government Area is 20.7 %.

The scrollable table below shows monthly data for the Noosa LGA going back to 2001.

LGA_data[,c(1:3, 6:7)] %>%  
  kbl(caption = "Noosa solar installs and capacity",
                      col.names = c("Date",
                                   "Installs",
                                   "kW",
                                   "Installs",
                                   "kW"),
                        format.args = list(big.mark = ",",scientific = FALSE)) %>% 
    add_header_above(c(" " = 1, "per month" = 2, "total" = 2)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = F, position = "left",
                font_size = 12,
                fixed_thead = T) %>%
scroll_box(width="50%", height = "450px")
Noosa solar installs and capacity
per month
total
Date Installs kW Installs kW
2022-06-30 65 573 13,974 76,016
2022-05-31 67 599 13,909 75,443
2022-04-30 70 663 13,842 74,844
2022-03-31 90 900 13,771 74,181
2022-02-28 66 608 13,681 73,281
2022-01-31 58 460 13,616 72,673
2021-12-31 110 988 13,558 72,213
2021-11-30 106 1,137 13,448 71,225
2021-10-31 98 920 13,343 70,088
2021-09-30 107 1,068 13,245 69,168
2021-08-31 123 1,177 13,138 68,100
2021-07-31 118 978 13,015 66,923
2021-06-30 127 1,038 12,897 65,945
2021-05-31 111 1,065 12,770 64,907
2021-04-30 122 985 12,659 63,842
2021-03-31 120 1,026 12,537 62,857
2021-02-28 146 1,219 12,418 61,831
2021-01-31 84 632 12,272 60,612
2020-12-31 153 1,528 12,187 59,980
2020-11-30 145 1,254 12,035 58,452
2020-10-31 125 1,011 11,890 57,198
2020-09-30 127 1,100 11,765 56,187
2020-08-31 121 1,104 11,638 55,087
2020-07-31 134 1,115 11,516 53,983
2020-06-30 123 1,008 11,382 52,868
2020-05-31 133 1,134 11,259 51,860
2020-04-30 107 816 11,127 50,725
2020-03-31 169 1,485 11,020 49,909
2020-02-29 122 920 10,850 48,424
2020-01-31 100 796 10,728 47,505
2019-12-31 128 1,052 10,628 46,709
2019-11-30 129 1,009 10,501 45,656
2019-10-31 109 937 10,372 44,647
2019-09-30 98 757 10,263 43,710
2019-08-31 76 562 10,165 42,953
2019-07-31 114 980 10,089 42,391
2019-06-30 93 637 9,975 41,412
2019-05-31 102 819 9,882 40,775
2019-04-30 84 685 9,780 39,955
2019-03-31 91 819 9,697 39,270
2019-02-28 85 603 9,606 38,451
2019-01-31 80 570 9,521 37,848
2018-12-31 94 825 9,441 37,279
2018-11-30 120 854 9,347 36,454
2018-10-31 85 675 9,227 35,600
2018-09-30 72 561 9,141 34,925
2018-08-31 71 520 9,069 34,364
2018-07-31 86 559 8,998 33,844
2018-06-30 88 783 8,912 33,284
2018-05-31 81 618 8,824 32,501
2018-04-30 73 617 8,742 31,883
2018-03-31 78 515 8,670 31,266
2018-02-28 67 420 8,592 30,751
2018-01-31 56 323 8,525 30,330
2017-12-31 89 570 8,469 30,007
2017-11-30 80 500 8,380 29,438
2017-10-31 90 547 8,300 28,937
2017-09-30 89 525 8,211 28,391
2017-08-31 91 637 8,122 27,866
2017-07-31 75 352 8,031 27,229
2017-06-30 67 473 7,956 26,876
2017-05-31 65 395 7,890 26,404
2017-04-30 67 320 7,825 26,008
2017-03-31 91 468 7,757 25,688
2017-02-28 72 340 7,666 25,220
2017-01-31 43 217 7,594 24,880
2016-12-31 82 556 7,552 24,663
2016-11-30 66 373 7,470 24,107
2016-10-31 67 390 7,404 23,734
2016-09-30 58 371 7,337 23,343
2016-08-31 54 302 7,279 22,973
2016-07-31 45 252 7,224 22,671
2016-06-30 53 316 7,179 22,419
2016-05-31 43 236 7,126 22,102
2016-04-30 45 211 7,083 21,867
2016-03-31 45 207 7,038 21,656
2016-02-29 48 361 6,993 21,448
2016-01-31 34 167 6,946 21,087
2015-12-31 47 218 6,912 20,921
2015-11-30 47 240 6,865 20,703
2015-10-31 54 252 6,818 20,462
2015-09-30 47 217 6,764 20,211
2015-08-31 45 237 6,717 19,994
2015-07-31 58 267 6,672 19,757
2015-06-30 65 299 6,614 19,490
2015-05-31 68 376 6,549 19,191
2015-04-30 60 343 6,481 18,816
2015-03-31 70 264 6,421 18,472
2015-02-28 52 209 6,352 18,209
2015-01-31 51 234 6,299 18,000
2014-12-31 58 218 6,248 17,766
2014-11-30 65 255 6,190 17,547
2014-10-31 62 231 6,126 17,292
2014-09-30 80 331 6,064 17,062
2014-08-31 55 204 5,984 16,731
2014-07-31 63 266 5,929 16,527
2014-06-30 72 293 5,866 16,261
2014-05-31 61 253 5,793 15,968
2014-04-30 58 267 5,732 15,714
2014-03-31 52 204 5,675 15,447
2014-02-28 45 169 5,623 15,243
2014-01-31 35 144 5,579 15,075
2013-12-31 38 166 5,543 14,931
2013-11-30 50 158 5,505 14,765
2013-10-31 45 170 5,455 14,607
2013-09-30 65 245 5,409 14,437
2013-08-31 58 177 5,345 14,193
2013-07-31 89 319 5,287 14,016
2013-06-30 170 693 5,198 13,697
2013-05-31 112 434 5,027 13,004
2013-04-30 85 293 4,915 12,570
2013-03-31 95 319 4,830 12,278
2013-02-28 58 247 4,736 11,958
2013-01-31 63 222 4,678 11,711
2012-12-31 154 562 4,615 11,489
2012-11-30 116 380 4,460 10,927
2012-10-31 118 358 4,345 10,548
2012-09-30 141 449 4,227 10,189
2012-08-31 174 544 4,086 9,740
2012-07-31 138 426 3,911 9,196
2012-06-30 289 924 3,774 8,770
2012-05-31 190 587 3,485 7,846
2012-04-30 139 430 3,295 7,259
2012-03-31 107 299 3,156 6,829
2012-02-29 100 289 3,049 6,529
2012-01-31 45 120 2,950 6,240
2011-12-31 85 284 2,905 6,121
2011-11-30 76 207 2,820 5,837
2011-10-31 55 137 2,744 5,630
2011-09-30 76 204 2,689 5,494
2011-08-31 70 171 2,614 5,290
2011-07-31 91 231 2,544 5,118
2011-06-30 328 834 2,453 4,888
2011-05-31 273 663 2,125 4,054
2011-04-30 124 277 1,852 3,391
2011-03-31 157 326 1,728 3,114
2011-02-28 115 253 1,571 2,789
2011-01-31 78 175 1,456 2,536
2010-12-31 73 183 1,378 2,361
2010-11-30 89 196 1,305 2,178
2010-10-31 65 151 1,216 1,983
2010-09-30 69 144 1,151 1,832
2010-08-31 85 175 1,082 1,688
2010-07-31 92 172 997 1,513
2010-06-30 90 132 905 1,341
2010-05-31 47 102 815 1,210
2010-04-30 52 98 768 1,107
2010-03-31 45 87 716 1,010
2010-02-28 86 116 671 922
2010-01-31 62 79 586 807
2009-12-31 77 113 524 728
2009-11-30 65 91 447 614
2009-10-31 48 68 382 524
2009-09-30 25 30 334 456
2009-08-31 39 50 308 427
2009-07-31 50 58 270 377
2009-06-30 35 43 220 319
2009-05-31 24 27 185 276
2009-04-30 18 18 161 249
2009-03-31 21 28 143 230
2009-02-28 8 14 122 202
2009-01-31 14 32 114 188
2008-12-31 9 9 100 156
2008-11-30 8 11 91 147
2008-10-31 12 17 83 136
2008-09-30 17 20 71 119
2008-08-31 8 11 54 99
2008-07-31 8 16 45 88
2008-06-30 4 10 37 72
2008-05-31 4 9 33 63
2008-04-30 3 3 29 54
2008-03-31 2 3 26 51
2008-02-29 3 5 24 48
2008-01-31 2 3 21 43
2007-12-31 2 8 19 40
2007-11-30 1 2 17 31
2007-10-31 0 0 15 29
2007-09-30 1 4 15 29
2007-08-31 2 3 14 25
2007-07-31 0 0 12 22
2007-06-30 0 0 12 22
2007-05-31 0 0 12 22
2007-04-30 0 0 12 22
2007-03-31 0 0 12 22
2007-02-28 0 0 12 22
2007-01-31 0 0 12 22
2006-12-31 0 0 12 22
2006-11-30 0 0 12 22
2006-10-31 0 0 12 22
2006-09-30 0 0 12 22
2006-08-31 0 0 12 22
2006-07-31 1 4 12 22
2006-06-30 0 0 11 18
2006-05-31 0 0 11 18
2006-04-30 0 0 11 18
2006-03-31 0 0 11 18
2006-02-28 0 0 11 18
2006-01-31 0 0 11 18
2005-12-31 0 0 11 18
2005-11-30 0 0 11 18
2005-10-31 0 0 11 18
2005-09-30 0 0 11 18
2005-08-31 0 0 11 18
2005-07-31 0 0 11 18
2005-06-30 1 2 11 18
2005-05-31 0 0 10 17
2005-04-30 0 0 10 17
2005-03-31 0 0 10 17
2005-02-28 0 0 10 17
2005-01-31 0 0 10 17
2004-12-31 1 1 10 17
2004-11-30 0 0 9 15
2004-10-31 0 0 9 15
2004-09-30 0 0 9 15
2004-08-31 1 1 9 15
2004-07-31 1 1 8 15
2004-06-30 0 0 7 14
2004-05-31 1 1 7 14
2004-04-30 1 2 6 13
2004-03-31 0 0 5 11
2004-02-29 1 1 5 11
2004-01-31 0 0 4 10
2003-12-31 0 0 4 10
2003-11-30 0 0 4 10
2003-10-31 0 0 4 10
2003-09-30 0 0 4 10
2003-08-31 1 2 4 10
2003-07-31 1 2 3 9
2003-06-30 1 3 2 7
2003-05-31 0 0 1 4
2003-04-30 0 0 1 4
2003-03-31 0 0 1 4
2003-02-28 0 0 1 4
2003-01-31 0 0 1 4
2002-12-31 0 0 1 4
2002-11-30 0 0 1 4
2002-10-31 0 0 1 4
2002-09-30 1 4 1 4
2002-08-31 0 0 0 0
2002-07-31 0 0 0 0
2002-06-30 0 0 0 0
2002-05-31 0 0 0 0
2002-04-30 0 0 0 0
2002-03-31 0 0 0 0
2002-02-28 0 0 0 0
2002-01-31 0 0 0 0
2001-12-31 0 0 0 0
2001-11-30 0 0 0 0
2001-10-31 0 0 0 0
2001-09-30 0 0 0 0
2001-08-31 0 0 0 0
2001-07-31 0 0 0 0
2001-06-30 0 0 0 0
2001-05-31 0 0 0 0
2001-04-30 0 0 0 0

Growth of Noosa LGA solar capacity

Showing only data from the beginning of 2010.

Instructions: you can click in the graph area, and then hover near the graph line to see actual values

Assumption: The percentage of data for postcode 4573 that is attributed to the Noosa Local Government Area is 20.7 %.

#
# should be able to refactor this for clarity !
#
# then should be able to parameterise, so same code can be used for different
# locality / postcode datasets
#


p <- LGA_data %>%
  filter(year_month>ymd("2010/01/01")) %>%
ggplot( 
       aes(x=year_month,y=Total_PV_kW)) +

  # geom_point(size = 0.2,
  #            aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE)))) +
    geom_line() +
  labs(title = "Noosa LGA Solar Capacity by year",
    subtitle = "as at 31 October 2021",
    caption = "Data from Clean Energy Regulator",
        x = "year",
    y = "kW installed") +
  theme_bw() +
  theme(text=element_text(size = 16))
p <- p %>% style(line = list(color = 'green'))
ggplotly(p,
#         aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), #format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE))),
         tooltip = "text") %>% 
    config(displaylogo = FALSE, # collaborate = FALSE, - deprecated
         modeBarButtonsToRemove = c(
           'sendDataToCloud',
           'autoScale2d', 
           'resetScale2d', 
 #          'toggleSpikelines',
           'hoverClosestCartesian', 
           'hoverCompareCartesian',
           'zoom2d',
           'pan2d',
           'select2d',
           'lasso2d',
           'zoomIn2d',
           'zoomOut2d'
         ))

Growth of Noosa LGA solar installations

Showing only data from the beginning of 2010.

Instructions: you can click in the graph area, and then hover near the graph line to see actual values

Assumption: The percentage of data for postcode 4573 that is attributed to the Noosa Local Government Area is 20.7 %.

p <- LGA_data %>%
  filter(year_month>ymd("2010/01/01")) %>%
ggplot( 
       aes(x=year_month,y=Total_PV_qty)) +

  # geom_point(size = 0.2,
  #            aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE)))) +
    geom_line() +
  labs(title = "Noosa LGA Solar Installations by year",
    subtitle = "as at 31 October 2021",
    caption = "Data from Clean Energy Regulator",
        x = "year",
    y = "installations") +
  theme_bw() +
  theme(text=element_text(size = 16))
p <- p %>% style(line = list(color = 'green'))
ggplotly(p,
#         aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), #format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE))),
         tooltip = "text") %>% 
    config(displaylogo = FALSE, # collaborate = FALSE, - deprecated
         modeBarButtonsToRemove = c(
           'sendDataToCloud',
           'autoScale2d', 
           'resetScale2d', 
 #          'toggleSpikelines',
           'hoverClosestCartesian', 
           'hoverCompareCartesian',
           'zoom2d',
           'pan2d',
           'select2d',
           'lasso2d',
           'zoomIn2d',
           'zoomOut2d'
         ))

Solar Hot Water Installations

Clean Energy Regulator reported number of installations of Solar Water Heaters is plotted below.

Assumption: The percentage of data for postcode 4573 that is attributed to the Noosa Local Government Area is 20.7 %.

Noosa LGA Solar Water Heaters (Rooftop)

p <- LGA_data %>%
#  filter(year_month>ymd("2010/01/01")) %>%  
ggplot( 
       aes(x=year_month,y=Total_SWH_qty)) +

  # geom_point(size = 0.2,
  #            aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE)))) +
    geom_line() +
  labs(title = "Noosa LGA Solar Water Heater Installs",
    subtitle = "as at 31 October 2021",
    caption = "Data from Clean Energy Regulator",
        x = "year",
    y = "installations") +
  theme_bw() +
  theme(text=element_text(size = 16))
p <- p %>% style(line = list(color = 'green'))
ggplotly(p,
#         aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), #format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE))),
         tooltip = "text") %>% 
    config(displaylogo = FALSE, # collaborate = FALSE, - deprecated
         modeBarButtonsToRemove = c(
           'sendDataToCloud',
           'autoScale2d', 
           'resetScale2d', 
 #          'toggleSpikelines',
           'hoverClosestCartesian', 
           'hoverCompareCartesian',
           'zoom2d',
           'pan2d',
           'select2d',
           'lasso2d',
           'zoomIn2d',
           'zoomOut2d'
         ))

Noosa LGA Solar Water Heaters - Air sourced heat pumps(ASHP)

p <- LGA_data %>%
#  filter(year_month>ymd("2010/01/01")) %>%  
ggplot( 
       aes(x=year_month,y=Total_SWHASHP_qty)) +

  # geom_point(size = 0.2,
  #            aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE)))) +
    geom_line() +
  labs(title = "Noosa LGA ASHP Solar Water Heater Installs",
    subtitle = "as at 31 October 2021",
    caption = "Data from Clean Energy Regulator",
        x = "year",
    y = "installations") +
  theme_bw() +
  theme(text=element_text(size = 16))
p <- p %>% style(line = list(color = 'green'))
ggplotly(p,
#         aes(text=sprintf("%s<br>%s kW", format(year_month, format="%d %B %Y"), #format(round(Total_PV_kW,0), nsmall=0, big.mark = ",", scientific = FALSE))),
         tooltip = "text") %>% 
    config(displaylogo = FALSE, # collaborate = FALSE, - deprecated
         modeBarButtonsToRemove = c(
           'sendDataToCloud',
           'autoScale2d', 
           'resetScale2d', 
 #          'toggleSpikelines',
           'hoverClosestCartesian', 
           'hoverCompareCartesian',
           'zoom2d',
           'pan2d',
           'select2d',
           'lasso2d',
           'zoomIn2d',
           'zoomOut2d'
         ))

Energex Customer Connection data

The data Energex currently make available publicly goes back to the start of 2016.

The Local Government Area data is based on the geo-location of customer address locations.

The table below shows the number of different types of Energex Customer Connections for the Noosa LGA

Note that a Customer Connection corresponds to the number of National Metering Identifier. The National Metering Identifier (NMI) is a unique 10 or 11 digit number used to identify every electricity network connection point in Australia

# read in data from saved rds file
rdsfilename = "EQld_consumption_Noosa_LGA.rds"
Noosa_LGA_consump <- read_rds(rdsfilename)

# just get Noosa LGA from this
myNoosaLGA <- Noosa_LGA_consump %>% filter(locality == 'NOOSA SHIRE')

myNoosaLGA[,c(2, 6:8)] %>%  
  kbl(caption = "Energex Customer Connections",
                      col.names = c("Date",
                                   "Business",
                                   "Residential",
                                   "Solar"),
                        format.args = list(big.mark = ",",scientific = FALSE)) %>% 
#    add_header_above(c(" " = 1, "per month" = 2, "total" = 2)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = F, position = "left",
                font_size = 12,
                fixed_thead = T) %>%
scroll_box(width="50%", height = "450px")
Energex Customer Connections
Date Business Residential Solar
2021-12-31 2,853 29,332 10,691
2021-11-30 2,867 29,332 10,645
2021-10-31 2,867 29,305 10,585
2021-09-30 2,862 29,277 10,516
2021-08-31 2,869 29,267 10,441
2021-07-31 2,862 29,251 10,347
2021-06-30 2,857 29,203 10,257
2021-05-31 2,858 29,203 10,198
2021-04-30 2,847 29,212 10,089
2021-03-31 2,833 29,192 10,019
2021-02-28 2,837 29,177 9,930
2021-01-31 2,833 29,179 9,825
2020-12-31 2,824 29,153 9,755
2020-11-30 2,830 29,160 9,684
2020-10-31 2,815 29,147 9,578
2020-09-30 2,806 29,122 9,470
2020-08-31 2,812 29,051 9,382
2020-07-31 2,800 29,060 9,283
2020-06-30 2,788 29,020 9,188
2020-05-31 2,798 28,983 9,100
2020-04-30 2,753 28,974 8,990
2020-03-31 2,749 28,964 8,865
2020-02-29 2,763 28,966 8,763
2020-01-31 2,763 28,956 8,674
2019-12-31 2,756 28,928 8,602
2019-11-30 2,761 28,937 8,549
2019-10-31 2,755 28,895 8,473
2019-09-30 2,741 28,855 8,369
2019-08-31 2,751 28,831 8,311
2019-07-31 2,744 28,805 8,229
2019-06-30 2,736 28,763 8,133
2019-05-31 2,756 28,741 8,079
2019-04-30 2,738 28,726 8,004
2019-03-31 2,732 28,676 7,917
2019-02-28 2,753 28,676 7,871
2019-01-31 2,741 28,649 7,809
2018-12-31 2,725 28,595 7,730
2018-11-30 2,737 28,616 7,678
2018-10-31 2,720 28,591 7,603
2018-09-30 2,702 28,558 7,535
2018-08-31 2,715 28,551 7,478
2018-07-31 2,711 28,524 7,402
2018-06-30 2,706 28,490 7,312
2018-05-31 2,712 28,464 6,814
2018-04-30 2,710 28,453 7,250
2018-03-31 2,714 28,382 7,194
2018-02-28 2,726 28,407 7,164
2018-01-31 2,724 28,390 7,092
2017-12-31 2,719 28,345 7,045
2017-11-30 2,731 28,371 7,031
2017-10-31 2,716 28,350 6,936
2017-09-30 2,702 28,331 6,865
2017-08-31 2,686 28,281 6,809
2017-07-31 2,670 28,259 6,754
2017-06-30 2,670 28,211 6,695
2017-05-31 2,682 28,228 6,659
2017-04-30 2,669 28,171 6,600
2017-03-31 2,668 28,149 6,568
2017-02-28 2,676 28,124 6,491
2017-01-31 2,680 28,087 6,453
2016-12-31 2,674 28,058 6,381
2016-11-30 2,668 28,022 6,344
2016-10-31 2,637 27,992 6,288
2016-09-30 2,637 27,974 6,230
2016-08-31 2,650 27,882 6,193
2016-07-31 2,630 27,841 6,139
2016-06-30 2,626 27,815 6,105
2016-05-31 2,637 27,857 6,080
2016-04-30 2,630 27,804 6,038
2016-03-31 2,617 27,773 5,998
2016-02-29 2,630 27,769 5,988
2016-01-31 2,616 27,735 5,962

AEMO Distributed Energy Resources data

Each Distributed Network Service Provider(DNSP), like Energex, submit reports to AEMO on a quarterly basis and what is termed ā€œDistributed Energy Resourcesā€.

This includes rooftop solar systems and battery systems, for both business and residential customers, down to postcode level.

Solar PV installed in the Noosa LGA

There are various sources of data for tracking the number of installations and capacity of solar PV systems.

Clean Energy Regulator(CER)

The Clean Energy Regulator manages the issuing of Small Technology Certificates which is the mechanism for the incentivising renewable energy systems.

The Clean Energy Regulator provides data on a monthly basis down to postcode level.

Reference - http://www.cleanenergyregulator.gov.au/RET/Forms-and-resources/Postcode-data-for-small-scale-installations

The Noosa LGA maps closely to postcodes 4563, 4565, 4566, 4567, 4568, 4569, 4571, and includes the Peregian Beach and Marcus Beach localities of postcode 4573.

A factor of 20% is used to estimate the Noosa LGA part of postcode 4573, based on historical data from Energex at the locality level. A live reference canā€™t be given as Energex no longer make this information available publicly.

In addition to small scale solar, the CER also publishes monthly data on the numbers of Solar Water Heaters, and Air-Sourced Heat Pump Solar Water Heaters. Solar Water Heaters refer to passive rooftop systems, and Air-Sourced Heat Pump Solar Water Heaters which heats water using energy from the ambient air, similar to a reverse cycle air-conditioner.

Energex

Energex is responsible for the approval of all solar PV systems to their network.

Energex provide data on electricity consumption and solar exported to the grid, by residential customers, business customer, and customers with solar.

This information is provided at an LGA and postcode level, on a quarterly basis.

The LGA information is based on the geo-location of the customer address, so will be accurate rather then inferred.

Reference - https://www.energex.com.au/about-us/company-information/our-network/energy-usage-data-to-share

Australian Energy Market Operator(AEMO)

AEMO manage electricity and gas systems and markets across Australia.

Each Distributed Network Service Provider(DNSP), like Energex, submit reports to AEMO on a quarterly basis and what is termed ā€œDistributed Energy Resourcesā€.

This includes rooftop solar systems and battery systems, for both business and residential customers, down to postcode level.

Reference - https://aemo.com.au/energy-systems/electricity/der-register/data-der/data-downloads

This page show the most recent data available from these sources, together with any assumptions made when presenting consolidated data at the Local Government Area level.

Noosa Local Government Area and Postcodes

Click on the coloured areas of the map. Popups will show the LGA area, or the postcode.

Note that the postcode is shown, for example for 4567, in the format QLD456731.

So to get the actual postcode, ignore the QLD and the trailing digits ā€œ31ā€

The LGA boundaries are shown in green.

Use the + - buttons to zoom, and click the icon below the + - buttons to change the background map. ā€œOpenStreetMapā€ will show locations and a street map.

The close correspondance between the LGA and postcode boundaries is apparent.

# borrowed this code from EQld_NetworkMaps.rmd

# define some global variables for folders
# but hardwire the location as a shortcut
# base_folder <- getwd()
maps_folder <- "/Users/geremida/Dropbox/ZENE/R/EQld_NetworkMaps/Energex_maps/"

myLGA <- readRDS(file=paste0(maps_folder,"Queensland_LGA_map.rds"))

# just get Gympie, Noosa & Sunshine Coast
NoosaLGA <- myLGA %>% filter(LGA_CODE %in% c('3620','5740','6720'))

myPostcodes <- readRDS(file=paste0(maps_folder,"Queensland_Postcode_map.rds"))

NoosaPostcodes <- myPostcodes %>% filter(PC_PID %in% c('QLD456331', 'QLD456531','QLD456631', 'QLD456731','QLD456831', 'QLD456931','QLD457131', 'QLD457331'))

tmap_mode(mode = "view")
## tmap mode set to interactive viewing
tm_shape(NoosaLGA)+
tm_polygons(col = "MAP_COLORS", alpha = 0.1,border.col = "chartreuse", lwd = 4) +
tm_shape(NoosaPostcodes)+
tm_polygons(col = "MAP_COLORS", alpha = 0.2, border.col = "darkmagenta")

data analysis to comeā€¦.

Notes

csv header problem has been fixed for June 2022 data

used same hack for Mar 2022 to fix extra space in header problem

did same hack for Feb 22 & Mar 22 & Apr 22 contacting Bryant, Darren again to request fix

Need to check PV kW data for Noosa postcodes for Feb 2022

Table is showing 0kW installed for Feb 2022 Found problem was extra space inserted by CER for Feb-22 header for kW column Temporary fix is to adjust csv file to remove extra space Sent message to CER