1. IEEE Region 10 Humanitarian Technology Conference 2017
A smart grid prerequisite: survey on electricity demand forecasting models and scope analysis of demand forecasting in Bangladesh
Samiul Islam1,2*
Amin Ahsan Ali1
Moinul Zaber1
1Data & Design Lab, University of Dhaka, Bangladesh
2BRAC University, Bangladesh
*Presenter
Funding Source
Data Facilitator
IEEE Region 10 Humanitarian Technology Conference 2017

2. IEEE Region 10 Humanitarian Technology Conference 2017
Motivation
Chance to work with real data
Chance to work in the betterment of the society
No research has been performed at this granular level in Bangladesh
Characteristics of Big data and it’s prospects
IEEE Region 10 Humanitarian Technology Conference 2017

3. Electricity Sector of Dhaka
Electricity distribution is maintained by two organization in Dhaka (North and South) City Corporation
DESCO (North)
DPDC (South)
Zone
Sub station
Feeder
Household
Agargaon
IEEE Region 10 Humanitarian Technology Conference 2017

4. Load Data Description Hourly load under each feeder Power factor
Handwritten data (Shah Ali Zone)
Kalyanpur substation: from 15/05/2015 to 21/06/2016
Shah Ali substation: from 15/05/2015 to 23/10/2016
Digitized data (Shah Ali Zone)
Kalyanpur substation: 15/05/2015 ~ 21/05/2015 & 11/12/2015 ~ 17/12/2015 & 01/01/2016 ~ 07/01/2016
Shah Ali Substation: 15/05/2015 ~ 21/05/2015 & 01/12/2015 ~ 21/12/2015
IEEE Region 10 Humanitarian Technology Conference 2017

5. Load Shedding Data Description
Day
Date
Feeder name
Start time
End time
Duration
Cause* of load shedding
Load shedding data of Shah Ali substation of Shah Ali zone for year 2015 (digitized)
*3 Reasons of load shedding: Preplanned
Sudden rise in demand
System failure
IEEE Region 10 Humanitarian Technology Conference 2017

6. IEEE Region 10 Humanitarian Technology Conference 2017
Road Map
Smart grid and necessity of a study on forecasting techniques
Review of recent forecasting scenarios
Scope analysis of Bangladesh
Legacy method works on an aggregate data
Load variability
Load shedding patterns and impacts
Two short term load forecasting models and performance comparison
IEEE Region 10 Humanitarian Technology Conference 2017

7. Smart Grid and Necessity of a Study on Demand Forecasting
Smart Grid (SG) channels a two-way communication between the customers and the utility
Collects the data from consumer end and can act accurately
Dynamic billing
Distribution of the total load over the day to avoid load shedding/blackout
What makes a grid smart?
Sensors and devices to collect granular level data
An efficient and real time data analysis technique to produce optimum decisions
How this research can help?
How forecasting models work
Understanding load variability
Seasonal and occasional impacts
Reasons and impacts of load shedding
Overall, a preparation
IEEE Region 10 Humanitarian Technology Conference 2017

8. Review of Recent Forecasting Scenarios
Turkey
40 years
No subgrouping
Taiwan
1 year
Weekday, weekend and holiday
Korea
2 weeks
Weekday and weekend
Spain
1 year
Autumn, Winter, Spring and Summer
IEEE Region 10 Humanitarian Technology Conference 2017

9. IEEE Region 10 Humanitarian Technology Conference 2017
Supply/Demand (Max) Balance in a Day: in 2015, available capacity was enough to satisfy maximum demand
IEEE Region 10 Humanitarian Technology Conference 2017

10. IEEE Region 10 Humanitarian Technology Conference 2017
Lost in Translation
Issues of legacy method of forecasting
Specific field wise data is not sent
Aggregate data (hard to understand feeder wise variability)
Absence of secondary data sources
Policy level
Aggregate load
Secondary sources
Substation
Feeder wise record
IEEE Region 10 Humanitarian Technology Conference 2017

11. IEEE Region 10 Humanitarian Technology Conference 2017
Hourly Average Load Comparison of Kalyanpur and Shah Ali Sub Station between Winter and Summer
Kalyanpur sub station
Shah Ali sub station
IEEE Region 10 Humanitarian Technology Conference 2017

12. Load Shedding Reasons of Shah Ali Zone in 2016
Reasons of load shedding
Pre-Scheduled load-shedding proposed by NLDC
Sudden rise in demand
System failure
Sudden rise in demand
IEEE Region 10 Humanitarian Technology Conference 2017

13. Load Shedding Statistics (Weekly and Daily) of Shah Ali Zone in 2016
Comparison Between Different Days in a Week
IEEE Region 10 Humanitarian Technology Conference 2017

14. Forecasting Model 5 input model
Data Period: 15th May 2015 to 21st May 2015 (1 week)
Training
Testing
15th May 2015 to 20th May 2015 (6 days)
21st May 2015
IEEE Region 10 Humanitarian Technology Conference 2017

15. Predictive output comparison (known set) 5 input model
1600
Actual output
Predictive output
1400
1200
Hourly Load (kw)
1000
800
600
400 50
100
150
Hours (Day wise grouped: Friday to Wednesday)

16. Predictive output comparison (unknown set) 5 input model
1400
Actual output
1300
Predictive output
1200
1100
Hourly Load (kw)
1000
900
800
700 5 10 15 20 25
Hours of 21st May 2015, Thursday

17. Forecasting Model 8 input model
Data Period: 15th May 2015 to 21st May 2015 (1 week)
Training
Testing
15th May 2015 to 20th May 2015 (6 days)
21st May 2015
IEEE Region 10 Humanitarian Technology Conference 2017

18. Predictive output comparison (known set) 8 input model
1500
Actual output
1400
Predictive output
1300
1200
1100
1000
Hourly Load (kw)
900
800
700
600
500 50
100
150
Hours (Day wise grouped: Friday to Wednesday)

19. Predictive output comparison (unknown set) 8 input model
1400
Actual output
1300
Predictive output
1200
1100
Hourly Load (kw)
1000
900
800
700 5 10 15 20 25
Hours of 21st May 2015, Thursday

20. Performance Analysis 5 input model 8 input model Accuracy*
5 input model
8 input model
Accuracy*
Dataset known to model:
( ) = %
Dataset unknown to model:
( ) = %
( ) = %
( ) = %
𝑀𝐴𝑃𝐸= 100 𝑛 𝑗=1 𝑛 | 𝑦 𝑗 − 𝑦 𝑗 𝑦 𝑗 |
* 𝑦 𝑗 & 𝑦 𝑗 are predictive and actual value relatively
*For accuracy calculation, MAPE has been used here
IEEE Region 10 Humanitarian Technology Conference 2017

21. IEEE Region 10 Humanitarian Technology Conference 2017
Final Resolution
NOT to promote ‘ready to implement’ models
To prove that load variability exists and impact of load shedding highly varies even in a small geographical area
Taking this into account, a better forecasting can be achieved
Load shedding cannot be eliminated, but might be reduced
IEEE Region 10 Humanitarian Technology Conference 2017

22. Thank You