324.RENEWABLE POWER PLANT ANALYSIS USING DATA STEP | PROC FORMAT | PROC SQL | PROC MEANS | PROC TABULATE | PROC CORR | PROC UNIVARIATE | PROC SGPLOT | MACROS

RENEWABLE POWER PLANT ANALYSIS USING DATA STEP | PROC FORMAT | PROC SQL | PROC MEANS | PROC TABULATE | PROC CORR | PROC UNIVARIATE | PROC SGPLOT | MACROS

 options nocenter;

1) CREATE RAW DATASET: renewable_plants_raw

data work.renewable_plants_raw;

    informat Installed_Date date9.;

    format   Installed_Date date9.;

    input Plant_Name :& $40. Country    :& $20. Technology :$8. Capacity_MW

        Installed_Date :date9. Cost_USD_Million Carbon_Reduction_TonsPerYear 

        Efficiency_PCT;

    Year_Installed = year(Installed_Date);

datalines;

Sunrise Solar        India        Solar     150 01JAN2016 120.5 180000 20.5

Azure Ridge         USA          Wind      200 15JUN2014 250.0 250000 33.8

RiverFlow           Brazil       Hydro     300 10MAR2010 480.0 400000 45.2

DesertSun           UAE          Solar     120 20SEP2019 95.0 150000 22.7

CoastalBreeze       UK           Wind       90 05NOV2018 110.0 90000  29.1

HighlandHydro       Canada       Hydro      75 12MAY2015 140.0 90000  42.0

ValleySun           Spain        Solar      60 01APR2020 45.0 70000   19.4

GaleFarm            Germany      Wind      130 30AUG2012 180.0 160000 31.6

MountainFlow        Norway       Hydro     110 22FEB2011 220.0 160000 46.5

SunPeak             Australia    Solar      45 17JUL2017 36.0 50000   21.0

WindEdge            India        Wind      160 03OCT2013 190.0 210000 34.2

BlueDam             China        Hydro     210 29DEC2008 400.0 320000 44.1

;

run;

proc print data=work.renewable_plants_raw(obs=12) noobs label;

    title "RENEWABLE PLANTS - RAW DATA (12 OBS)";

    var Plant_Name Country Technology Capacity_MW Installed_Date

        Year_Installed Cost_USD_Million Carbon_Reduction_TonsPerYear Efficiency_PCT;

run;

OUTPUT:

RENEWABLE PLANTS - RAW DATA (12 OBS)

Plant_Name	Country	Technology	Capacity_MW	Installed_Date	Year_Installed	Cost_USD_Million	Carbon_Reduction_TonsPerYear	Efficiency_PCT
Sunrise Solar	India	Solar	150	01JAN2016	2016	120.5	180000	20.5
Azure Ridge	USA	Wind	200	15JUN2014	2014	250.0	250000	33.8
RiverFlow	Brazil	Hydro	300	10MAR2010	2010	480.0	400000	45.2
DesertSun	UAE	Solar	120	20SEP2019	2019	95.0	150000	22.7
CoastalBreeze	UK	Wind	90	05NOV2018	2018	110.0	90000	29.1
HighlandHydro	Canada	Hydro	75	12MAY2015	2015	140.0	90000	42.0
ValleySun	Spain	Solar	60	01APR2020	2020	45.0	70000	19.4
GaleFarm	Germany	Wind	130	30AUG2012	2012	180.0	160000	31.6
MountainFlow	Norway	Hydro	110	22FEB2011	2011	220.0	160000	46.5
SunPeak	Australia	Solar	45	17JUL2017	2017	36.0	50000	21.0
WindEdge	India	Wind	160	03OCT2013	2013	190.0	210000	34.2
BlueDam	China	Hydro	210	29DEC2008	2008	400.0	320000	44.1

2) DERIVATIONS: Age, Next_Maintenance_Date using INTCK / INTNX

data work.renewable_plants;

  set work.renewable_plants_raw;

  /* Age in completed years */

  Age_Years = intck('year', Installed_Date, today(), 'c'); /* 'c' count completed boundaries */

  /* Days since installed (useful for more precise calculations) */

  Days_Since_Installed = intck('day', Installed_Date, today());

  /* Next scheduled maintenance (example every 5 years from installation) */

  Next_Maintenance_Date = intnx('year', Installed_Date, ceil(Age_Years/5)*5, 'same');

  format Next_Maintenance_Date date9.;

  /* Round numeric variables to sensible precision */

  Capacity_MW = round(Capacity_MW, 0.1);

  Efficiency_PCT = round(Efficiency_PCT, 0.1);

run;

/* Validate derived dates and ages */

proc print data=work.renewable_plants(obs=12) label noobs;

  title "RENEWABLE PLANTS - DERIVED FIELDS";

  var Plant_Name Installed_Date Age_Years Days_Since_Installed Next_Maintenance_Date;

run;

OUTPUT:

RENEWABLE PLANTS - DERIVED FIELDS

Plant_Name	Installed_Date	Age_Years	Days_Since_Installed	Next_Maintenance_Date
Sunrise Solar	01JAN2016	9	3621	01JAN2026
Azure Ridge	15JUN2014	11	4186	15JUN2029
RiverFlow	10MAR2010	15	5744	10MAR2025
DesertSun	20SEP2019	6	2263	20SEP2029
CoastalBreeze	05NOV2018	7	2582	05NOV2028
HighlandHydro	12MAY2015	10	3855	12MAY2025
ValleySun	01APR2020	5	2069	01APR2025
GaleFarm	30AUG2012	13	4840	30AUG2027
MountainFlow	22FEB2011	14	5395	22FEB2026
SunPeak	17JUL2017	8	3058	17JUL2027
WindEdge	03OCT2013	12	4441	03OCT2028
BlueDam	29DEC2008	16	6180	29DEC2028

3) MACRO: Efficiency classification

      - Efficiency_Class: 'Low', 'Medium', 'High'

%macro classify_efficiency(in_ds=work.renewable_plants, out_ds=work.renewable_plants_class,

                           low_thr=20, high_thr=40);

  proc format;

    value efffmt

      low - < &low_thr = 'Low'

      &low_thr - < &high_thr = 'Medium'

      &high_thr - high = 'High'

    ;

  run;

  data &out_ds;

    set &in_ds;

    /* apply thresholds: careful about boundary values */

    if Efficiency_PCT < &low_thr then Efficiency_Class = 'Low';

    else if Efficiency_PCT < &high_thr then Efficiency_Class = 'Medium';

    else Efficiency_Class = 'High';

    /* also stash a formatted value */

    Efficiency_Class_F = put(Efficiency_PCT, efffmt.);

  run;

  proc print data=&out_ds;

  run;

%mend classify_efficiency;

%classify_efficiency();

OUTPUT:

Obs	Installed_Date	Plant_Name	Country	Technology	Capacity_MW	Cost_USD_Million	Carbon_Reduction_TonsPerYear	Efficiency_PCT	Year_Installed	Age_Years	Days_Since_Installed	Next_Maintenance_Date	Efficiency_Class	Efficiency_Class_F
1	01JAN2016	Sunrise Solar	India	Solar	150	120.5	180000	20.5	2016	9	3621	01JAN2026	Med	Medium
2	15JUN2014	Azure Ridge	USA	Wind	200	250.0	250000	33.8	2014	11	4186	15JUN2029	Med	Medium
3	10MAR2010	RiverFlow	Brazil	Hydro	300	480.0	400000	45.2	2010	15	5744	10MAR2025	Hig	High
4	20SEP2019	DesertSun	UAE	Solar	120	95.0	150000	22.7	2019	6	2263	20SEP2029	Med	Medium
5	05NOV2018	CoastalBreeze	UK	Wind	90	110.0	90000	29.1	2018	7	2582	05NOV2028	Med	Medium
6	12MAY2015	HighlandHydro	Canada	Hydro	75	140.0	90000	42.0	2015	10	3855	12MAY2025	Hig	High
7	01APR2020	ValleySun	Spain	Solar	60	45.0	70000	19.4	2020	5	2069	01APR2025	Low	Low
8	30AUG2012	GaleFarm	Germany	Wind	130	180.0	160000	31.6	2012	13	4840	30AUG2027	Med	Medium
9	22FEB2011	MountainFlow	Norway	Hydro	110	220.0	160000	46.5	2011	14	5395	22FEB2026	Hig	High
10	17JUL2017	SunPeak	Australia	Solar	45	36.0	50000	21.0	2017	8	3058	17JUL2027	Med	Medium
11	03OCT2013	WindEdge	India	Wind	160	190.0	210000	34.2	2013	12	4441	03OCT2028	Med	Medium
12	29DEC2008	BlueDam	China	Hydro	210	400.0	320000	44.1	2008	16	6180	29DEC2028	Hig	High

/* Check classification */

proc freq data=work.renewable_plants_class;

  tables Efficiency_Class Efficiency_Class_F / nocum nopercent;

  title "EFFICIENCY CLASSIFICATION - FREQUENCY";

run;

OUTPUT:

EFFICIENCY CLASSIFICATION - FREQUENCY

The FREQ Procedure

Efficiency_Class	Frequency
Hig	4
Low	1
Med	7

Efficiency_Class_F	Frequency
High	4
Low	1
Medium	7

4) PROC SQL: summary tables by Technology and by Country

proc sql;

  create table work.summary_by_tech as

  select Technology,

         count(*) as N_PowerPlants,

         sum(Capacity_MW) as Total_Capacity_MW format=comma12.1,

         mean(Efficiency_PCT) as Mean_Efficiency_PCT format=8.2,

         sum(Carbon_Reduction_TonsPerYear) as Total_Carbon_Reduction format=comma12.,

         mean(Cost_USD_Million) as Mean_Cost_Million format=comma12.2

  from work.renewable_plants_class

  group by Technology

  order by Total_Capacity_MW desc;

quit;

proc print data=work.summary_by_tech noobs label;

  title "SUMMARY BY TECHNOLOGY (PROC SQL)";

run;

OUTPUT:

SUMMARY BY TECHNOLOGY (PROC SQL)

Technology	N_PowerPlants	Total_Capacity_MW	Mean_Efficiency_PCT	Total_Carbon_Reduction	Mean_Cost_Million
Hydro	4	695.0	44.45	970,000	310.00
Wind	4	580.0	32.18	710,000	182.50
Solar	4	375.0	20.90	450,000	74.13

/* Summary by country */

proc sql;

  create table work.summary_by_country as

  select Country,

         count(*) as N_PowerPlants,

         sum(Capacity_MW) as Total_Capacity_MW format=comma12.1,

         mean(Efficiency_PCT) as Mean_Efficiency_PCT format=8.2

  from work.renewable_plants_class

  group by Country

  order by Total_Capacity_MW desc;

quit;

proc print data=work.summary_by_country noobs label;

  title "SUMMARY BY COUNTRY (PROC SQL)";

run;

OUTPUT:

SUMMARY BY COUNTRY (PROC SQL)

Country	N_PowerPlants	Total_Capacity_MW	Mean_Efficiency_PCT
India	2	310.0	27.35
Brazil	1	300.0	45.20
China	1	210.0	44.10
USA	1	200.0	33.80
Germany	1	130.0	31.60
UAE	1	120.0	22.70
Norway	1	110.0	46.50
UK	1	90.0	29.10
Canada	1	75.0	42.00
Spain	1	60.0	19.40
Australia	1	45.0	21.00

5) PROC MEANS: Descriptive statistics by Technology

proc means data=work.renewable_plants_class n mean std min max median maxdec=2;

  class Technology;

  var Capacity_MW Efficiency_PCT Cost_USD_Million Carbon_Reduction_TonsPerYear Age_Years;

  title "DESCRIPTIVE STATISTICS BY TECHNOLOGY (PROC MEANS)";

run;

OUTPUT:

DESCRIPTIVE STATISTICS BY TECHNOLOGY (PROC MEANS)

The MEANS Procedure

Technology	N Obs	Variable	N	Mean	Std Dev	Minimum	Maximum	Median
Hydro	4	Capacity_MW Efficiency_PCT Cost_USD_Million Carbon_Reduction_TonsPerYear Age_Years	4 4 4 4 4	173.75 44.45 310.00 242500.00 13.75	101.77 1.91 157.06 142448.82 2.63	75.00 42.00 140.00 90000.00 10.00	300.00 46.50 480.00 400000.00 16.00	160.00 44.65 310.00 240000.00 14.50
Solar	4	Capacity_MW Efficiency_PCT Cost_USD_Million Carbon_Reduction_TonsPerYear Age_Years	4 4 4 4 4	93.75 20.90 74.13 112500.00 7.00	49.56 1.37 40.37 62383.22 1.83	45.00 19.40 36.00 50000.00 5.00	150.00 22.70 120.50 180000.00 9.00	90.00 20.75 70.00 110000.00 7.00
Wind	4	Capacity_MW Efficiency_PCT Cost_USD_Million Carbon_Reduction_TonsPerYear Age_Years	4 4 4 4 4	145.00 32.18 182.50 177500.00 10.75	46.55 2.35 57.37 68980.67 2.63	90.00 29.10 110.00 90000.00 7.00	200.00 34.20 250.00 250000.00 13.00	145.00 32.70 185.00 185000.00 11.50

6) PROC TABULATE: Nicely formatted report

     - Cross-tab: Technology x Efficiency_Class with sums and means

proc tabulate data=work.renewable_plants_class format=8.2 noseps missing;

  class Technology Efficiency_Class;

  var Capacity_MW Efficiency_PCT Cost_USD_Million Carbon_Reduction_TonsPerYear;

  table Technology,

        (Capacity_MW*sum='Total Capacity (MW)'

         Efficiency_PCT*mean='Avg Efficiency (%)'

         Cost_USD_Million*mean='Avg Cost (M$)')*f=comma12.2

        / rts=20;

  keylabel sum='Sum' mean='Mean';

  title "TABULATED SUMMARY: TECHNOLOGY x METRICS (PROC TABULATE)";

run;

OUTPUT:

TABULATED SUMMARY: TECHNOLOGY x METRICS (PROC TABULATE)

	Capacity_MW	Efficiency_PCT	Cost_USD_Million
	Total Capacity (MW)	Avg Efficiency (%)	Avg Cost (M$)
Technology	695.00	44.45	310.00
Hydro
Solar	375.00	20.90	74.13
Wind	580.00	32.18	182.50

7) PROC SGPLOT: Visualizations

     - Bar chart: Total Capacity by Country

     - Scatter: Efficiency vs Capacity, sized by Carbon_Reduction

     - Series or vbar for count by Efficiency_Class

/* Prepare aggregated dataset for plotting capacity by country */

proc sql;

  create table work.plot_capacity_by_country as

  select Country, sum(Capacity_MW) as Total_Capacity_MW

  from work.renewable_plants_class

  group by Country;

quit;

proc print data=work.plot_capacity_by_country;

run;

OUTPUT:

Obs	Country	Total_Capacity_MW
1	Australia	45
2	Brazil	300
3	Canada	75
4	China	210
5	Germany	130
6	India	310
7	Norway	110
8	Spain	60
9	UAE	120
10	UK	90
11	USA	200

proc sgplot data=work.plot_capacity_by_country;

  vbar Country / response=Total_Capacity_MW datalabel;

  yaxis label="Total Capacity (MW)";

  title "TOTAL CAPACITY BY COUNTRY (MW)";

run;

OUTPUT:

proc sgplot data=work.renewable_plants_class;

  bubble x=Capacity_MW y=Efficiency_PCT size=Carbon_Reduction_TonsPerYear / datalabel=Plant_Name;

  xaxis label="Capacity (MW)";

  yaxis label="Efficiency (%)";

  title "EFFICIENCY vs CAPACITY (BUBBLE = CARBON REDUCTION)";

run;

OUTPUT:

proc sgplot data=work.renewable_plants_class;

  vbar Efficiency_Class / stat=freq datalabel;

  xaxis label="Efficiency Class";

  yaxis label="Number of Plants";

  title "NUMBER OF PLANTS BY EFFICIENCY CLASS";

run;

OUTPUT:

8) PROC CORR: Check correlation between key performance metrics

proc corr data=work.renewable_plants_class plots=matrix(histogram);

   var Capacity_MW Cost_USD_Million Efficiency_PCT Carbon_Reduction_TonsPerYear Age_Years;

   title "CORRELATION ANALYSIS OF RENEWABLE PLANT METRICS (PROC CORR)";

run;

OUTPUT:

CORRELATION ANALYSIS OF RENEWABLE PLANT METRICS (PROC CORR)

The CORR Procedure

5 Variables:	Capacity_MW Cost_USD_Million Efficiency_PCT Carbon_Reduction_TonsPerYear Age_Years

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Capacity_MW	12	137.50000	72.66048	1650	45.00000	300.00000
Cost_USD_Million	12	188.87500	134.93469	2267	36.00000	480.00000
Efficiency_PCT	12	32.50833	10.19335	390.10000	19.40000	46.50000
Carbon_Reduction_TonsPerYear	12	177500	104718	2130000	50000	400000
Age_Years	12	10.50000	3.60555	126.00000	5.00000	16.00000

Pearson Correlation Coefficients, N = 12 Prob > |r| under H0: Rho=0
	Capacity_MW	Cost_USD_Million	Efficiency_PCT	Carbon_Reduction_TonsPerYear	Age_Years
Capacity_MW	1.00000	0.91379 <.0001	0.52954 0.0766	0.98718 <.0001	0.69228 0.0126
Cost_USD_Million	0.91379 <.0001	1.00000	0.78379 0.0026	0.94888 <.0001	0.85754 0.0004
Efficiency_PCT	0.52954 0.0766	0.78379 0.0026	1.00000	0.59440 0.0415	0.84335 0.0006
Carbon_Reduction_TonsPerYear	0.98718 <.0001	0.94888 <.0001	0.59440 0.0415	1.00000	0.75724 0.0043
Age_Years	0.69228 0.0126	0.85754 0.0004	0.84335 0.0006	0.75724 0.0043	1.00000

9) PROC UNIVARIATE: Distribution, normality, outliers Using OUTPUT statement for percentiles

proc univariate data=work.renewable_plants_class noprint;

   var Capacity_MW Efficiency_PCT Carbon_Reduction_TonsPerYear;

   histogram Capacity_MW / normal;

   inset n mean std min max / position=ne;

   title "DISTRIBUTION AND OUTLIER ANALYSIS (PROC UNIVARIATE)";

run;

OUTPUT:

DISTRIBUTION AND OUTLIER ANALYSIS (PROC UNIVARIATE)

The UNIVARIATE Procedure

---

DISTRIBUTION AND OUTLIER ANALYSIS (PROC UNIVARIATE)

The UNIVARIATE Procedure

Fitted Normal Distribution for Capacity_MW

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	137.5
Std Dev	Sigma	72.66048

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.12840999	Pr > D	>0.150
Cramer-von Mises	W-Sq	0.03540932	Pr > W-Sq	>0.250
Anderson-Darling	A-Sq	0.26628388	Pr > A-Sq	>0.250

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	45.0000	-31.5336
5.0	45.0000	17.9841
10.0	60.0000	44.3818
25.0	82.5000	88.4913
50.0	125.0000	137.5000
75.0	180.0000	186.5087
90.0	210.0000	230.6182
95.0	300.0000	257.0159
99.0	300.0000	306.5336

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