Designing, Debugging, and Detecting Fraud in a Smart Grid Load Balancing System Using Advanced SAS

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | INPUT | DATALINES | IF-THEN-ELSE | OUTPUT | DELETE | SET | MERGE | PROC SORT | PROC APPEND | PROC TRANSPOSE | PROC MEANS | PROC FREQ | PROC SQL | PROC REPORT | PROC COMPARE | %MACRO | %MEND

Introduction

In today’s energy-driven world, smart grid systems play a critical role in balancing electricity demand and supply. Grid load balancing ensures stable power distribution across regions by monitoring demand (MW), renewable contribution, storage usage, system frequency deviation, and outage events. However, real-world datasets often contain intentional or unintentional errors such as invalid date formats, incorrect merges, misclassified utilization levels, inconsistent character formats, and fraudulent manipulations in reporting, validation layers, QC comparisons, advanced macro automation, rolling metrics, anomaly scoring, and audit-ready reporting structure.

In this project, we will:

Create a Grid Load Balancing dataset
Introduce intentional errors
Detect and correct those errors
Use SAS macros for fraud detection
Apply date functions (MDY, INTCK, INTNX)
Use SET, MERGE, APPEND, TRANSPOSE
Apply numeric and character functions
Use PROC DATASETS DELETE
Provide full explanation of every step

Table of Contents

Business Context
Dataset Design
Intentional Errors
Full Corrected SAS Code
Macro Fraud Detection Logic
Date Handling (MDY, INTCK, INTNX)
Data Manipulation (SET, MERGE, APPEND, TRANSPOSE)
Character Functions
Numeric Functions
Classification Logic
Error Debugging Section
Rolling 3-Month Stability Trend
Advanced Fraud Scoring Model
Duplicate Record Detection
Data Validation Macro
QC Comparison Program
Risk Categorization Layer
Outlier Detection Using Z-Score
Monthly Aggregation Dataset
Dynamic Reporting Macro
Audit Trail Dataset
Conclusion

Business Context

Electric grid operators monitor:

Demand in MW
Renewable percentage
Battery storage usage
Frequency stability (50 Hz standard in India)
Outage counts
Stability Index (calculated score)

Fraud or manipulation may occur when:

Renewable share is inflated
Storage usage misreported
Frequency deviation hidden
Dates manipulated
Duplicate records appended

Build system → Introduce errors → Detect → Correct → Final clean dataset.

1. Create Raw Dataset (With Intentional Errors)

data grid_load_raw;

input Grid_Region $ Demand_MW Renewable_Share Storage_Usage Frequency_Deviation

Outage_Events Stability_Index Month Day Year;

format Grid_Date date9.;

Grid_Date = mdy(Month, Day, Year);

Utilization_Percentage = Demand_MW / 1000 * 100;

datalines;

north 1200 45 30 0.02 1 85 01 15 2025

south 1500 60 40 0.05 2 90 02 20 2025

east 1800 110 50 0.10 3 70 03 25 2025

west 900 30 20 0.50 5 60 04 10 2025

central 1300 55 35 . 0 88 05 05 2025

north 1250 45 30 0.02 1 85 13 15 2025

south 1400 60 40 0.03 2 92 06 31 2025

east 2000 50 45 0.08 4 75 07 18 2025

west 950 35 25 0.02 1 89 08 22 2025

central 1600 65 50 0.01 0 95 09 12 2025

north 1700 48 38 0.03 2 82 10 28 2025

south 1800 70 55 0.09 3 78 11 15 2025

east 1900 52 42 0.04 1 91 12 05 2025

;

run;

proc print data=grid_load_raw;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage
1	north	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	120
2	south	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	150
3	east	1800	110	50	0.10	3	70	3	25	2025	25MAR2025	180
4	west	900	30	20	0.50	5	60	4	10	2025	10APR2025	90
5	central	1300	55	35	.	0	88	5	5	2025	05MAY2025	130
6	north	1250	45	30	0.02	1	85	13	15	2025	.	125
7	south	1400	60	40	0.03	2	92	6	31	2025	.	140
8	east	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	200
9	west	950	35	25	0.02	1	89	8	22	2025	22AUG2025	95
10	central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	160
11	north	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	170
12	south	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	180
13	east	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	190

❌ Intentional Errors

· Renewable_Share = 110 (Impossible)

· Frequency_Deviation missing

· Month = 13 (Invalid)

· June 31 (Invalid date)

· Utilization formula simplistic

· No classification

· Lowercase region inconsistencies

2. Clean & Correct Dataset

data grid_load_clean;

set grid_load_raw;

length Utilization_Class $12.;

Grid_Region = propcase(strip(Grid_Region));

Renewable_Share = min(Renewable_Share,100);

if missing(Frequency_Deviation) then Frequency_Deviation = 0;

if Month > 12 then delete;

if Month=6 and Day>30 then Day=30;

Grid_Date = mdy(Month, Day, Year);

Months_Since_Start = intck('month','01JAN2025'd,Grid_Date);

Next_Month = intnx('month',Grid_Date,1);

Utilization_Percentage = round((Demand_MW/2000)*100,0.1);

if Utilization_Percentage > 85 then Utilization_Class="High";

else if Utilization_Percentage > 60 then Utilization_Class="Medium";

else Utilization_Class="Low";

Fraud_Flag = 0;

if Renewable_Share > 95 and Stability_Index < 70 then Fraud_Flag=1;

if Frequency_Deviation > 0.3 then Fraud_Flag=1;

if Outage_Events > 4 and Stability_Index > 90 then Fraud_Flag=1;

run;

proc print data=grid_load_clean;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
2	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
5	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
6	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
7	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
8	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0
9	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
10	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
11	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
12	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0

`set grid_load_raw;`

Reads raw dataset.

`propcase(strip())`

Removes spaces and standardizes case.

`min(Renewable_Share,100);`

Caps impossible values.

`missing()`

Handles null frequency.

`intck`

Counts months difference.

`intnx`

Calculates next month date.

Classification logic

Categorizes grid load.

3. SeparateFraud Detection Macro

%macro fraud_check(dataset);

data fraud_flagged;

set &dataset;

Fraud_Flag = 0;

if Renewable_Share > 95 and Stability_Index < 70 then Fraud_Flag=1;

if Frequency_Deviation > 0.3 then Fraud_Flag=1;

if Outage_Events > 4 and Stability_Index > 90 then Fraud_Flag=1;

run;

proc print data=fraud_flagged;

run;

%mend;

%fraud_check(grid_load_clean);

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
2	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
5	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
6	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
7	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
8	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0
9	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
10	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
11	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
12	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0

· Reusable logic

· Scalable to multiple datasets

· Centralized fraud rules

4. Append & Merge

data grid_backup;

set grid_load_clean;

run;

proc print data=grid_backup;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
2	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
5	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
6	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
7	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
8	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0
9	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
10	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
11	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
12	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0

proc append base=grid_backup

data=grid_load_clean force;

run;

proc print data=grid_backup;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
2	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
5	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
6	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
7	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
8	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0
9	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
10	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
11	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
12	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0
13	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
14	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
15	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
16	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
17	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
18	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
19	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
20	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0
21	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
22	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
23	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
24	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0

· Demonstrates duplication risk.

Merge Example

data region_capacity;

input Grid_Region $ Capacity_MW;

datalines;

North 2000

South 2200

East 2100

West 1500

Central 1800

;

run;

proc print data=region_capacity;

run;

OUTPUT:


Obs	Grid_Region	Capacity_MW
1	North	2000
2	South	2200
3	East	2100
4	West	1500
5	Central	1800

proc sort data=grid_load_clean; by Grid_Region; run;

proc print data=grid_load_clean;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0

proc sort data=region_capacity; by Grid_Region; run;

proc print data=region_capacity;

run;

OUTPUT:


Obs	Grid_Region	Capacity_MW
1	Central	1800
2	East	2100
3	North	2000
4	South	2200
5	West	1500

data merged_grid;

merge grid_load_clean region_capacity;

by Grid_Region;

run;

proc print data=merged_grid;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Capacity_MW
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	1800
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	1800
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	2100
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	2100
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	2100
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	2000
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	2000
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	2200
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	2200
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	2200
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	1500
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	1500

5. Transpose

proc transpose data=merged_grid out=transposed_grid;

by Grid_Region;

var Demand_MW Renewable_Share;

run;

proc print data=transposed_grid;

run;

OUTPUT:


Obs	Grid_Region	_NAME_	COL1	COL2	COL3
1	Central	Demand_MW	1300	1600	.
2	Central	Renewable_Share	55	65	.
3	East	Demand_MW	1800	2000	1900
4	East	Renewable_Share	100	50	52
5	North	Demand_MW	1200	1700	.
6	North	Renewable_Share	45	48	.
7	South	Demand_MW	1500	1400	1800
8	South	Renewable_Share	60	60	70
9	West	Demand_MW	900	950	.
10	West	Renewable_Share	30	35	.

6. Rolling 3-Month Stability Trend

proc sort data=grid_load_clean;

by Grid_Region Grid_Date;

run;

proc print data=grid_load_clean;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0

data rolling_stability;

set grid_load_clean;

by Grid_Region;

retain stab_lag1 stab_lag2;

Rolling_3M_Stability = mean(Stability_Index, stab_lag1, stab_lag2);

stab_lag2 = stab_lag1;

stab_lag1 = Stability_Index;

run;

proc print data=rolling_stability;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	stab_lag1	stab_lag2	Rolling_3M_Stability
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	88	.	88.0000
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	95	88	91.5000
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	70	95	84.3333
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	75	70	80.0000
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	91	75	78.6667
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	85	91	83.6667
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	82	85	86.0000
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	90	82	85.6667
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	92	90	88.0000
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	78	92	86.6667
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	60	78	76.6667
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	89	60	75.6667

· RETAIN preserves prior values

· Computes moving average

· Detects gradual deterioration in grid stability

7. Advanced Fraud Risk Scoring

data fraud_scoring;

set grid_load_clean;

Fraud_Score = 0;

Fraud_Score + (Renewable_Share > 95) * 20;

Fraud_Score + (Frequency_Deviation > 0.3) * 30;

Fraud_Score + (Outage_Events > 4) * 25;

Fraud_Score + (Stability_Index < 65) * 25;

length Fraud_Level $12.;

if Fraud_Score >= 60 then Fraud_Level="High Risk";

else if Fraud_Score >= 30 then Fraud_Level="Medium Risk";

else Fraud_Level="Low Risk";

run;

proc print data=fraud_scoring;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Fraud_Score	Fraud_Level
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	0	Low Risk
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	0	Low Risk
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	20	Low Risk
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	0	Low Risk
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	0	Low Risk
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	0	Low Risk
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	0	Low Risk
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	0	Low Risk
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	0	Low Risk
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	0	Low Risk
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	80	High Risk
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	0	Low Risk

· Weighted scoring is more realistic

· Helps prioritize investigations

8. Duplicate Detection Logic

proc sort data=grid_load_clean out=dup_check nodupkey dupout=duplicates;

by Grid_Region Grid_Date;

run;

proc print data=dup_check;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0

· NODUPKEY removes duplicates

· DUPOUT captures suspicious duplicates

· Important after PROC APPEND operations

9. Data Validation Macro

%macro validate_data(dataset);

proc means data=&dataset n nmiss min max;

var Demand_MW Renewable_Share Frequency_Deviation Stability_Index;

run;

proc freq data=&dataset;

tables Utilization_Class Fraud_Flag / missing;

run;

%mend;

%validate_data(grid_load_clean);

OUTPUT:

The MEANS Procedure


Variable	N	N Miss	Minimum	Maximum
Demand_MW Renewable_Share Frequency_Deviation Stability_Index	12 12 12 12	0 0 0 0	900.0000000 30.0000000 0 60.0000000	2000.00 100.0000000 0.5000000 95.0000000

The FREQ Procedure


Utilization_Class	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	4	33.33	4	33.33
Low	3	25.00	7	58.33
Medium	5	41.67	12	100.00


Fraud_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
0	11	91.67	11	91.67
1	1	8.33	12	100.00

· Quick QC summary

· Detects missing values

· Validates ranges

10. Independent QC Comparison Program

proc compare base=grid_load_clean

compare=fraud_scoring

out=compare_results

outnoequal;

id Grid_Region Grid_Date;

run;

OUTPUT:

The COMPARE Procedure                                                                                                               
Comparison of WORK.GRID_LOAD_CLEAN with WORK.FRAUD_SCORING                                                                          
(Method=EXACT)                                                                                                                      
                                                                                                                                    
Data Set Summary                                                                                                                    
                                                                                                                                    
Dataset                        Created          Modified  NVar    NObs                                                              
                                                                                                                                    
WORK.GRID_LOAD_CLEAN  28FEB26:00:41:29  28FEB26:00:41:29    16      12                                                              
WORK.FRAUD_SCORING    28FEB26:00:41:45  28FEB26:00:41:45    18      12                                                              
                                                                                                                                    
                                                                                                                                    
Variables Summary                                                                                                                   
                                                                                                                                    
Number of Variables in Common: 16.                                                                                                  
Number of Variables in WORK.FRAUD_SCORING but not in WORK.GRID_LOAD_CLEAN: 2.                                                       
Number of ID Variables: 2.

                                                                                                                                    
                                                                                                                                    
Observation Summary                                                                                                                 
                                                                                                                                    
Observation      Base  Compare  ID                                                                                                  
                                                                                                                                    
First Obs           1        1  Grid_Region=Central Grid_Date=05MAY2025                                                             
Last  Obs          12       12  Grid_Region=West Grid_Date=22AUG2025                                                                
                                                                                                                                    
Number of Observations in Common: 12.                                                                                               
Total Number of Observations Read from WORK.GRID_LOAD_CLEAN: 12.                                                                    
Total Number of Observations Read from WORK.FRAUD_SCORING: 12.                                                                      
                                                                                                                                    
Number of Observations with Some Compared Variables Unequal: 0.                                                                     
Number of Observations with All Compared Variables Equal: 12.                                                                       
                                                                                                                                    
NOTE: No unequal values were found. All values compared are exactly equal.

· Standard industry QC practice

· Detects derivation mismatches

11. Z-Score Outlier Detection

proc means data=grid_load_clean mean std noprint;

var Demand_MW;

output out=stats mean=mean_d std=std_d;

run;

proc print data=stats;

run;

OUTPUT:


Obs	_TYPE_	_FREQ_	mean_d	std_d
1	0	12	1504.17	362.101

data zscore_check;

if _n_=1 then set stats;

set grid_load_clean;

Z_Demand = (Demand_MW - mean_d) / std_d;

if abs(Z_Demand) > 1 then Outlier_Flag="Y";

else Outlier_Flag="N";

run;

proc print data=zscore_check;

run;

OUTPUT:


Obs	_FREQ_	mean_d	std_d	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Z_Demand	Outlier_Flag
1	12	1504.17	362.101	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	-0.56384	N
2	12	1504.17	362.101	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	0.26466	N
3	12	1504.17	362.101	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	0.81699	N
4	12	1504.17	362.101	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	1.36932	Y
5	12	1504.17	362.101	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	1.09316	Y
6	12	1504.17	362.101	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	-0.84000	N
7	12	1504.17	362.101	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	0.54082	N
8	12	1504.17	362.101	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	-0.01151	N
9	12	1504.17	362.101	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	-0.28767	N
10	12	1504.17	362.101	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	0.81699	N
11	12	1504.17	362.101	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	-1.66850	Y
12	12	1504.17	362.101	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	-1.53042	Y

· Identifies statistical anomalies

· Important for fraud analytics

12. Monthly Aggregation Dataset

proc sql;

create table monthly_summary as

select Grid_Region,

intnx('month', Grid_Date, 0, 'b') format=date9. as Month_Start,

mean(Demand_MW) as Avg_Demand,

mean(Stability_Index) as Avg_Stability,

sum(Outage_Events) as Total_Outages

from grid_load_clean

group by Grid_Region, calculated Month_Start;

quit;

proc print data=monthly_summary;

run;

OUTPUT:


Obs	Grid_Region	Month_Start	Avg_Demand	Avg_Stability	Total_Outages
1	Central	01MAY2025	1300	88	0
2	Central	01SEP2025	1600	95	0
3	East	01MAR2025	1800	70	3
4	East	01JUL2025	2000	75	4
5	East	01DEC2025	1900	91	1
6	North	01JAN2025	1200	85	1
7	North	01OCT2025	1700	82	2
8	South	01FEB2025	1500	90	2
9	South	01JUN2025	1400	92	2
10	South	01NOV2025	1800	78	3
11	West	01APR2025	900	60	5
12	West	01AUG2025	950	89	1

· Supports management dashboards

· Groups data by month

· Uses INTNX with beginning option

13. Risk Categorization Layer

data risk_profile;

set fraud_scoring;

Risk_Index = mean(Fraud_Score, Utilization_Percentage);

length Risk_Category $12.;

if Risk_Index > 80 then Risk_Category="Critical";

else if Risk_Index > 60 then Risk_Category="Severe";

else if Risk_Index > 40 then Risk_Category="Moderate";

else Risk_Category="Stable";

run;

proc print data=risk_profile;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Fraud_Score	Fraud_Level	Risk_Index	Risk_Category
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	0	Low Risk	32.50	Stable
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	0	Low Risk	40.00	Stable
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	20	Low Risk	55.00	Moderate
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	0	Low Risk	50.00	Moderate
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	0	Low Risk	47.50	Moderate
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	0	Low Risk	30.00	Stable
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	0	Low Risk	42.50	Moderate
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	0	Low Risk	37.50	Stable
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	0	Low Risk	35.00	Stable
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	0	Low Risk	45.00	Moderate
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	80	High Risk	62.50	Severe
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	0	Low Risk	23.75	Stable

14. Dynamic Reporting Macro

%macro region_report(region_name);

proc report data=grid_load_clean nowd;

where Grid_Region="&region_name";

column Grid_Date Demand_MW Stability_Index Fraud_Flag;

define Grid_Date / display;

define Demand_MW / analysis mean;

define Stability_Index / analysis mean;

run;

%mend;

%region_report(North);

OUTPUT:


Grid_Date	Demand_MW	Stability_Index	Fraud_Flag
15JAN2025	1200	85	0
28OCT2025	1700	82	0

· Generates region-specific reporting

· Parameterized macro

· Reusable across regions

15. Audit Trail Dataset

data audit_trail;

length Action $50 User $20 Timestamp 8;

format Timestamp datetime20.;

Action="Fraud Score Generated";

User="SYSTEM";

Timestamp=datetime();

output;

run;

proc print data=audit_trail;

run;

OUTPUT:


Obs	Action	User	Timestamp
1	Fraud Score Generated	SYSTEM	28FEB2026:06:13:53

· Maintains audit trace

· Important in regulatory environments

16. Character Function Advanced Usage

data character_demo;

set grid_load_clean;

Region_Upper = upcase(Grid_Region);

Region_Lower = lowcase(Grid_Region);

Region_Label = catx(" - ", propcase(Grid_Region), put(Year,4.));

Region_Clean = strip(trim(Grid_Region));

run;

proc print data=character_demo;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Region_Upper	Region_Lower	Region_Label	Region_Clean
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	CENTRAL	central	Central - 2025	Central
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	CENTRAL	central	Central - 2025	Central
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	EAST	east	East - 2025	East
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	EAST	east	East - 2025	East
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	EAST	east	East - 2025	East
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	NORTH	north	North - 2025	North
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	NORTH	north	North - 2025	North
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	SOUTH	south	South - 2025	South
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	SOUTH	south	South - 2025	South
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	SOUTH	south	South - 2025	South
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	WEST	west	West - 2025	West
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	WEST	west	West - 2025	West

17. Numeric Function Enhancements

data numeric_demo;

set grid_load_clean;

Demand_Squared = Demand_MW**2;

Demand_Log = log(Demand_MW);

Demand_Root = sqrt(Demand_MW);

Demand_Rounded = round(Demand_MW,10);

run;

proc print data=numeric_demo;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Demand_Squared	Demand_Log	Demand_Root	Demand_Rounded
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	1690000	7.17012	36.0555	1300
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	2560000	7.37776	40.0000	1600
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	3240000	7.49554	42.4264	1800
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	4000000	7.60090	44.7214	2000
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	3610000	7.54961	43.5890	1900
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	1440000	7.09008	34.6410	1200
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	2890000	7.43838	41.2311	1700
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	2250000	7.31322	38.7298	1500
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	1960000	7.24423	37.4166	1400
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	3240000	7.49554	42.4264	1800
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	810000	6.80239	30.0000	900
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	902500	6.85646	30.8221	950

18. Final Corrected Dataset Code

data final_grid_system;

set merged_grid;

Region_Code = catx("-",substr(Grid_Region,1,3),Year);

Adjusted_Stability = mean(Stability_Index,100-Frequency_Deviation*100);

Performance_Score = sum(Utilization_Percentage,

Renewable_Share,

Adjusted_Stability);

if Fraud_Flag=1 then Status="Under Review";

else Status="Stable";

run;

proc print data=final_grid_system;

run;

OUTPUT:


Obs	Grid_Region	Demand_MW	Renewable_Share	Storage_Usage	Frequency_Deviation	Outage_Events	Stability_Index	Month	Day	Year	Grid_Date	Utilization_Percentage	Utilization_Class	Months_Since_Start	Next_Month	Fraud_Flag	Capacity_MW	Region_Code	Adjusted_Stability	Performance_Score	Status
1	Central	1300	55	35	0.00	0	88	5	5	2025	05MAY2025	65.0	Medium	4	23893	0	1800	Cen-2025	94.0	214.0	Stable
2	Central	1600	65	50	0.01	0	95	9	12	2025	12SEP2025	80.0	Medium	8	24015	0	1800	Cen-2025	97.0	242.0	Stable
3	East	1800	100	50	0.10	3	70	3	25	2025	25MAR2025	90.0	High	2	23832	0	2100	Eas-2025	80.0	270.0	Stable
4	East	2000	50	45	0.08	4	75	7	18	2025	18JUL2025	100.0	High	6	23954	0	2100	Eas-2025	83.5	233.5	Stable
5	East	1900	52	42	0.04	1	91	12	5	2025	05DEC2025	95.0	High	11	24107	0	2100	Eas-2025	93.5	240.5	Stable
6	North	1200	45	30	0.02	1	85	1	15	2025	15JAN2025	60.0	Low	0	23773	0	2000	Nor-2025	91.5	196.5	Stable
7	North	1700	48	38	0.03	2	82	10	28	2025	28OCT2025	85.0	Medium	9	24046	0	2000	Nor-2025	89.5	222.5	Stable
8	South	1500	60	40	0.05	2	90	2	20	2025	20FEB2025	75.0	Medium	1	23801	0	2200	Sou-2025	92.5	227.5	Stable
9	South	1400	60	40	0.03	2	92	6	30	2025	30JUN2025	70.0	Medium	5	23923	0	2200	Sou-2025	94.5	224.5	Stable
10	South	1800	70	55	0.09	3	78	11	15	2025	15NOV2025	90.0	High	10	24076	0	2200	Sou-2025	84.5	244.5	Stable
11	West	900	30	20	0.50	5	60	4	10	2025	10APR2025	45.0	Low	3	23862	1	1500	Wes-2025	55.0	130.0	Under Review
12	West	950	35	25	0.02	1	89	8	22	2025	22AUG2025	47.5	Low	7	23985	0	1500	Wes-2025	93.5	176.0	Stable

19. Delete Dataset

proc datasets library=work;

delete grid_backup;

quit;

LOG:

 NOTE: Deleting WORK.GRID_BACKUP (memtype=DATA).

Intentional Error Explanation

Error	Why Wrong	Fix
Month=13	Invalid date	Delete record
Renewable 110	Impossible %	Capped to 100
Missing frequency	Null issue	Set to 0
June 31	Invalid day	Adjusted
Duplicate append	Data inflation	Delete backup

🔤 Character Functions

· strip()

· trim()

· cat()

· catx()

· propcase()

· upcase()

· lowcase()

· coalesce()

🔢 Numeric Functions

· round()

· min()

· max()

· abs()

· sum()

· mean()

Key Learning Points

· Data cleaning is essential.

· Date validation critical.

· Macros improve reusability.

· Character normalization prevents merge errors.

· Fraud detection requires logical thresholds.

· Append may cause duplication.

· Transpose useful for reporting.

· Numeric functions improve calculations.

Conclusion

Here we designed, debugged, and built a fraud-detection-enabled Smart Grid Load Balancing system using SAS.

· Created realistic dataset

· Introduced errors intentionally

· Corrected them

· Applied macro logic

· Used advanced date functions

· Applied character & numeric transformations

· Used SET, MERGE, APPEND, TRANSPOSE

· Managed datasets using PROC DATASETS

· Used RETAIN for rolling analytics

· Implemented weighted fraud scoring

· Built validation macro

· Performed independent QC using PROC COMPARE

· Applied statistical outlier detection

· Created reusable reporting macros

· Maintained audit trace dataset

This mirrors real-world energy analytics projects where grid stability is monitored daily and fraud detection safeguards reporting integrity.

SAS INTERVIEW QUESTIONS

1. 1.What is PROC MEANS used for in SAS?

2. 2.What is the difference between SET and MERGE statements?

3. 3.Why are macros used in SAS?

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About the Author:
SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:
The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent MONITORING DEMAND data.

Our Mission:
This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:
·  Students learning SAS
·  Data analysts building portfolios
·  Professionals preparing for SAS interviews
·  Bloggers writing about analytics
·  Clinical SAS Programmer
·  Research Data Analyst
·  Regulatory Data Validator

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414.Can We Design, Debug, and Detect Fraud in a Smart Grid Load Balancing System Using Advanced SAS Programming Techniques?

Designing, Debugging, and Detecting Fraud in a Smart Grid Load Balancing System Using Advanced SAS

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | INPUT | DATALINES | IF-THEN-ELSE | OUTPUT | DELETE | SET | MERGE | PROC SORT | PROC APPEND | PROC TRANSPOSE | PROC MEANS | PROC FREQ | PROC SQL | PROC REPORT | PROC COMPARE | %MACRO | %MEND

`set grid_load_raw;`

`propcase(strip())`

`min(Renewable_Share,100);`

`missing()`

`intck`

`intnx`

Classification logic

🔤 Character Functions

Conclusion

Follow Us On :

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project
2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project
3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project

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About Us | Contact | Privacy Policy

Comments

Post a Comment

Popular posts from this blog

412.Can We Build And Clean A University Course Analytics & Fraud Detection System In Sas Using Only Macros While Intentionally Creating And Fixing Errors?

409.Can We Build a Reliable Emergency Services Analytics & Fraud Detection System in SAS While Identifying and Fixing Intentional Errors?

397.If a satellite has excellent signal strength but very high latency, can it still deliver good quality communication? Why or why not?A Sas Study

414.Can We Design, Debug, and Detect Fraud in a Smart Grid Load Balancing System Using Advanced SAS Programming Techniques?

Designing, Debugging, and Detecting Fraud in a Smart Grid Load Balancing System Using Advanced SAS

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | INPUT | DATALINES | IF-THEN-ELSE | OUTPUT | DELETE | SET | MERGE | PROC SORT | PROC APPEND | PROC TRANSPOSE | PROC MEANS | PROC FREQ | PROC SQL | PROC REPORT | PROC COMPARE | %MACRO | %MEND

set grid_load_raw;

propcase(strip())

min(Renewable_Share,100);

missing()

intck

intnx

Classification logic

🔤 Character Functions

Conclusion

Follow Us On :

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

About Us | Contact | Privacy Policy

Comments

Post a Comment

Popular posts from this blog

412.Can We Build And Clean A University Course Analytics & Fraud Detection System In Sas Using Only Macros While Intentionally Creating And Fixing Errors?

409.Can We Build a Reliable Emergency Services Analytics & Fraud Detection System in SAS While Identifying and Fixing Intentional Errors?

397.If a satellite has excellent signal strength but very high latency, can it still deliver good quality communication? Why or why not?A Sas Study

`set grid_load_raw;`

`propcase(strip())`

`min(Renewable_Share,100);`

`missing()`

`intck`

`intnx`

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project
2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project
3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project