Presented during Uganda Open Data/Open Science National Dialogue 25-26 April 2018.

1. Application of Big Data in the
Energy Service Industry,
(Energy Efficiency & DSM)
Alexander Komakech-Akena, CEA. Eng.
Kampala
26.04.2018
National Dialogue on Mainstreaming Open Data Access
and Use in Uganda
25 -26th April 2018

2. Overview
1. This Big Picture
• Features of Bigdata
• Countries w. Pioneering Research
2. Narrowing the Picture
• Case I: Application in RE industry
• Case II: Application in DSM and EMS
AOT Consulting (Uganda)A. Komakech-Akena
This session focus on mainly application of large
data repositories in the energy industry –
particularly DSM and EM
Open data ???
The question is
always; “IS
THERE
GUARANTEED
SECURITY” of all
swath of data
collected?

3. What is Bigdata?
• “Volume” refers to the large data capacity, which is beyond the processing
capacity of conventional database software tool.
• “Variety” includes the variety of data types and data sources.
• “Velocity” has several meanings, including high growth rate of data, fast
data transfer, high data storage speed and processing speed, high real-time
demands, etc.
• “Value” means that the data has high value but the value density is low.
• “Energy” refers to that the value of electric power big data is increasing in
using and refining process, which can provide guidance for the energy
saving and loss reduction of electric power industry.
AOT Consulting (Uganda)A. Komakech-Akena

4. BigData
AOT Consulting (Uganda)A. Komakech-Akena
Time
Weat
her kV
area
gend
er kg
volu
me
Tariff kWh
kW
Hz BiG DATA
Volume
Value
Variety
Velocity
Characteristics of
Bigdata (4”Vs”)

5. Big Data Sources
Electric power big data has many resources and mainly comes from the
following information collection and management systems:
– Supervisory Control And Data Acquisition (SCADA) system,
– Energy Management System (EMS),
– Wide area measurement system (WAMS),
– Operation management system (OMS), Automatic Dispatching System,
– Distribution management system (DMS),
– Electricity Consumption Information collection system,
– Advanced Metering Infrastructure (AMI), tele-meter reading (TMR),
– power quality monitoring system, marketing business system, customer service
system, electricity trading platform,
– wind power and photovoltaic power prediction system,
– Production management system (PMS), management information system,
– enterprise resource planning (ERP),
– Geographic Information System (GIS),
– Weather Forecast System (WFS)
AOT Consulting (Uganda)A. Komakech-Akena

6. 6
A. Komakech-Akena
0.75
0.8
0.85
0.9
0.95
1
1.05
0
20000
40000
60000
80000
100000
120000
140000
160000
P.F
VA/W
Duration (1min interval)
PT+ (W Load) (1 min) QT (var) (1 min) ST (VA) (1 min) PFT+ (Load) (1 min)
Example: Load Profile

7. 7
A. Komakech-Akena
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100
110
120
130
140
150
kVA
1:10:00 PM
11/22/2017
5:05:00 PM
11/23/2017
5 h/Div
1:03:55:00 (d:h:min:s)
Start of day
ShoulderOff-peakPeak
Domestic
& GH Lighting
GH
All except
lighting in GH
Load Profile – Flower Farm

8. Big Data
AOT Consulting (Uganda)A. Komakech-Akena
Longlist of
potential large
data in the
electricity (200+)
Only the few studied
Generation Prediction (Wind and
Solar PV)
Low voltage detection
Risk assessment and Early warning of
distribution
Smart grids and meter
Energy monitoring systems
Specified on next page

9. Case of EE& DSM (Assessment)
AOT Consulting (Uganda)A. Komakech-Akena
Observe and
measure
Analyse
Historical with
present
Identification
of influentional
Opportunities
• What energy sources
input?
• Where is energy being
used?
• How much is being
consumed?
• What are climatic
conditions exist? (e.g. RH,
temperature)
• What is the cost (UGX) of a
kWh, litre, m3, kg?

10. CASE I: Coffee
Processing Factory

11. Base year (2016) – Energy Expenditure
11
A. Komakech-Akena
HFO
12%
Electricity
86%
Diesel
2% Elec. 533.24 MUGX
HFO 72.3 MUGX
Diesel 14.5 MUGX
MUGX is million Uganda Shillings
HFO
32%
Electricity
64%
Diesel
4%
Energy Source (Used)
Total Energy: 4,484 GJ pa
Observations

12. 12
A. Komakech-Akena
kVA,
demand is
lower than
energy
consumed
(kWh)
2016 – Electrical Energy Consumption
Observations

13. 13
A. Komakech-Akena
0.0000
0.0050
0.0100
0.0150
0.0200
0.0250
0.0300
0.0350
0.0400
0.0450
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
SPECIFIC CONSUMPTION
2017 – Electrical Energy Consumption
Best: 0.0207 kWh/kg
SEU is combines direct and
indirect energy in coffee
processing
Application

14. 14
A. Komakech-Akena
-
1,000,000.00
2,000,000.00
3,000,000.00
4,000,000.00
5,000,000.00
6,000,000.00
-
20,000.00
40,000.00
60,000.00
80,000.00
100,000.00
120,000.00
1 2 3 4 5 6 7 8 9 10 11 12
1
3
2
4
5
1
Electricalenergy
KilogramsofCoffeebeans
Inefficiency:
- Could be idle running of the
machines??
- Loads that are seldom used
coming on???
Specific Energy Consumption

15. 15
A. Komakech-Akena
0.00
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100
110
120
130
140
150
160
170
180
kVA
12:13:00.000 PM
7/4/2017
12:22:00.000 PM
7/5/2017
4 h/Div
1:00:09:00 (d:h:min:s)
Typical Load Profile

16. 16
A. Komakech-Akena
0
20
40
60
80
100
120
140
160
Compressors Hulling section Dryer Ware house
lighting
Production
Floor lights and
fans
Bulk loader Office Admin Grading,
destoning,
colour sorting
Security
lighting
Measured Energy Consumption
kW kVA
Measurements (Results)

17. 17
A. Komakech-Akena
y = 0.0166x + 19885
R² = 0.92488
-
20,000.00
40,000.00
60,000.00
80,000.00
100,000.00
120,000.00
140,000.00
- 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 7,000,000.00
Energy(kWh)
Produc on (Kg of Coffee)
Coef. Reg: 92.5%,
Base energy: 19,885
kWh
SEU: 0.0166 kWh/kg
Simple Regression Analysis
Energy Required (Y) = 0.0166
(Quantity produced) + Base energy

18. 18
A. Komakech-Akena
y = 0.0166x + 19885
R² = 0.92488
-
20,000.00
40,000.00
60,000.00
80,000.00
100,000.00
120,000.00
140,000.00
- 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 7,000,000.00
Energy(kWh)
Produc on (Kg of Coffee)
Coef. Reg: 92.5%,
Base energy: 19,885
kWh
SEU: 0.0166 kWh/kg
Simple Regression Analysis
Very strong
relationship

19. 19
A. Komakech-Akena
EMO- Energy Monitoring
1000kVA, 415V
11kV
Transformer
CB CB CB CB CB CB
Loading/Pre-cleaning
Grading,De-
stoning,sorting
Dryers
Administration
Compressors
Hulling
Energy
metering point
Circuit Breaker
KEY
Savings realised for
energy monitoring
systems installation =
10%
Advantage:
Ability to control and
change consumption
real time.

20. Demand Response
AOT Consulting (Uganda)A. Komakech-Akena
Actions
taken to shift
demand
Source: USAID DSM Program in Tanzania

21. 21
A. Komakech-Akena
Conclusion
1. Benefits of using bigdata increases efficiency and
lowers O&M cost
2. Its application is till at a toddler stage in Uganda
and East Africa
3. Demand Response – saving on demand= CO2
emission saving, LCOE generally cheaper

22. Asante!