1) The document analyzes energy efficiency for high-rise buildings using artificial neural networks. It focuses on two case study buildings, Skywarth and Skymage, which are 33 and 44 stories respectively. 2) The highest energy consumption in both buildings is lighting. Several potential energy efficiency approaches are identified, including lighting retrofits. 3) Using multilayer perceptron neural networks, the potential energy savings from retrofits are estimated at RM55,639 for Skywarth and RM147,388 for Skymage, with return on investment periods of 9 and 3 years respectively.

1. AN ANALYSIS OF ENERGY EFFICIENCY FOR HIGH
RISE BUILDING USING ARTIFICIAL NEURAL
NETWORK
STUDENT NAME: HARISON GIMANG ANAK RICHARD
ID NUMBER : 51210113779
SUPERVISORS : Ir MOHD FAIRUZ BIN ABDUL HAMID (Main Supervisor)
: DR NOR AZUANA BINTI RAMLI (Co-Supervisor)
: PUAN AZIZAH KASSIM (External Industrial Co-Supervisor)
Collaboration with:

2. INTRODUCTION

3. What is ENERGY EFFICIENCY?
Maintain the same OR less
amount of energy
consumption to produce the
better output
Process
(Equipment/Operations)
Using less energy to
accomplish the same task
and enjoy the same
comfort level
INPUT OUTPUT
-Electricity
-Temperature
-Coal
-Production
-Bills
-Quality
-Comfort
level

4. Why ENERGY EFFICIENCY?
Electricity Supply Act 1990
Efficient Management of Electrical Energy Regulations 2008
Section 3 (1A)
- Any installation which receives electrical energy from a licensee or supply authority with
a total electricity consumption equal to or exceeding 3,000,000 kWh as measured at one
metering point or more over any period of six consecutive months that will conducted by
REEM
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0
2,000
4,000
6,000
8,000
10,000
12,000
Electricity Consumption
(kWh/capital)
Source: Energy Commission and Economic
Planning Unit 2015
100,000
110,000
120,000
130,000
140,000
150,000
160,000
GWh
Year
Energy Demand: BAU vs NEEAP
BAU
NEEAP

5. Problem Statements
1.) Office building contributes to the largest energy consumption in Malaysia
(Sadaf Zeeshan, 2014)
2.( Increasing of the electricity tariff where 29.6 sen/kWh for the first 200kWh
and 37.2sen/kWh for the next kWh
(https://www.tnb.com.my/commercial-industrial/pricing-tariffs1)
3.)The increasing of carbon emissions which is 0.747kg per 1kWh emitted to
the atmosphere for each 1kWh electricity generated by power plant
(http://seda.gov.my)
4.) Lack of awareness monitoring on building energy management where lead
to inefficiency energy management
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6. To provide a guideline/benchmarking for industry in
improving the energy consumption and cost saving
To identify highest energy consumptions
systems/application in the building
To analyze and mitigation the EE using artificial neural
network prediction
OBJECTIVES
To investigate the potential energy efficiency approach in
the commercial building
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7. High rise building commercial building
-Skywarth Building 33 Storeys
-Skymage Building 44 Storeys
-Building system process
-Government policy certifications or standard
requirement
-Useful floor area of over 1000m2
Prediction based on artificial neural network
-Multi Layer Perceptron
-Radial Basic Function
Comparison with Traditional method
-Linear Regression
Scope Of Project
References standard
-ISO 50001 Energy
monitoring
-MS 1525
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8. LITERATURE REVIEW

9. • Software base CLTD
• Major energy utilization
• Divided into 11 areas
Case study optimization of
energy management in an
office building
(Sadaf Zeeshan, 2014)
• Various types of artificial neural network method
• Prediction based on heating, cooling, indoor air
etc.
• Each building with different method of ANN
Energy analysis of a
abuilding using artificial
neural network
(Kumar.R et al, 2013)
Previous Research
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10. METHODOLOGY

11. Research Framework
Audit energy consumption in the building
Monitor, target and compile data collection
(Energy consumption information)
Analyze the retrofitting impact on the active power
consumption
Prediction of energy consumption by using ANN
(SPSS Statistic software)
Comparison with mathematical method
Results
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12. Electricity Accounting
8.1, 8%
6, 6%
2.1, 2%
1.5, 2%
50.00%, 1%
1.3, 1%
0.1, 0%
37.4, 37%
24.4, 24%
18.6, 19%
80.5,
80%
Skywarth Building Energy
Accounting
Office Equipment & Small Power
Chilled Water Pumps
Elavators
Air Cooled Split
Mechanical Pump
Fan Coil Unit
Compound and Façade Lighting
Indoor Lighting
Air Handling Unit
3.2, 3%
11.6, 12%
1.1, 1%
2.6, 3%
3.4, 3%
2, 2%
34.4, 34%
16, 16%
10.7, 11%
15.1, 15%
76.2, 76%
Skymage Building Energy
Accounting
Chilled Water Pumps
Elavators
Air Cooled Split Unit
Fan Coil Unit
Mechanical Pump
Compound and Façade Lighting
2.0
Indoor Lighting
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13. Variable Parameter
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14. Classification of Energy Efficiency Investment
Through Energy Performance Contract (EPC)
1
NO/LOW COST
EASY
3
HIGH COST
EASY
2
NO/LOW COST
HARD
4
HIGH COST
HARD
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15. Potential Toward Energy Efficiency
Approach
(1) Control of
maximum demand
Skywarth and Skymage building
(2) Lamp De-lamping Skywarth and Skymage building
(1) BCS Reschedule
Operating Hour for
Lighting
Skywarth building
(1) 24 Hours Mobile
Workstation
Skywarth and Skymage building
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16. (3) Use of
Occupancy Sensor
Skywarth and Skymage building
(3) Energy
Awareness
Campaign
Skywarth and Skymage building
(2) Tariff
management
Skywarth and Skymage building
(3) Lighting
Retrofitting
Skywarth and Skymage building
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17. Based on biological brain
Multilayer Perceptron method
Radial Basic Function method
Data processing occurs at neurons
or nodes
Output a single based on the input
signal
Artificial Neural Network
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18. RESULTS
AND
DISCUSSION

19. Energy Prediction Comparison
0.00
100,000.00
200,000.00
300,000.00
400,000.00
500,000.00
600,000.00
ELECTRICITY
CONSUMPTI
ON
MONTH
Building Skywarth MLP vs RBF vs
Linear Regression
ELECTRICITY
CONSUMPTION
Linear Regression
MLP
RBF
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
Jan-13
Mar-13
May-13
Jul-13
Sep-13
Nov-13
Jan-14
Mar-14
May-14
Jul-14
Sep-14
Nov-14
Jan-15
Mar-15
May-15
Jul-15
ELECTRICITY
CONSUMPTI
ON
MONTH
Skymage Building MLP vs RBF vs
Linear Regression
ELECTRICITY CONSUMPTION
Liniear Regression
MLP
RBF
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20. Accuracy Comparison
Method Error Sqt
Skywarth Building
Multilayer Perceptron 24626.78954
Radial Basic Function 25480.459
Linear Regression 30491.231
(LARGEST)
Skymage Building
Multilayer Perceptron 57532.9886
Radial Basic Function 66970.32327
Linear Regression 91738.31389
(LARGEST)
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21. Cost Saving And BEI
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Skywarth Building BEI Baseline
Annual Electricity Consumption (Actual) (kWh) 5,907,068.00
Building GFA (exclude basement) (m²) 50,064.00
Building Nett Usable areas (m²) 38,187.00
BEI based on GFA (kWh/m²/year) 118.00
BEI based on Nett Usable/Lettable Areas
(kWh/m²/year) 155.00
Annual Electricity Consumption (MLP)(kWh) 5,754,632.00
BEI based on GFA (kWh/m²/year) 115.00
BEI based on Nett Usable/Lettable Areas
(kWh/m²/year) 151.00
Potential Saving (RM) 55,639.00
Energy Efficiency Retrofitting Approach Investment
(RM) 499,539.00
Return of Investment, ROI (Years) 9 Years
Sykmage Building BEI Baseline
Annual Electricity Consumption (Actual) (kWh) 9,219,390.00
Building GFA (exclude basement) (m²) 71,598.00
Building Nett Usable areas (m²) 54,005.00
BEI based on GFA (kWh/m²/year) 129.00
BEI based on Nett Usable/Lettable Areas
(kWh/m²/year) 171.00
Annual Electricity Consumption (MLP)(kWh) 8,815,588.00
BEI based on GFA (kWh/m²/year) 123.00
BEI based on Nett Usable/Lettable Areas
(kWh/m²/year) 163.00
Potential Saving (RM) 147,388.00
Energy Efficiency Retrofitting Approach Investment
(RM) 499,539.00
Return of Investment (Years) 3 Years

22. CONCLUSION
AND
RECOMMENDATIONS

23. CONCLUSION
From this research the highest energy consumption in both building is the lighting system.
There are 8 energy efficiency approach's that had been identify for the Skywarth building
and 7 approach's for the Skymage building and the approach that had been selected is
the lighting retrofitting. By using Multilayer Perceptron the amount of potential saving
for Skywarth and Skymage is worth RM55,639 and RM147,388 with the ROI of 9 years
and 3 years. This research also provide the BEI based on the selected baseline which is
January 2013 until December 2013 for both buildings by using actual value and the
prediction value that can be obtain by the energy efficiency method that had been
selected.
RECOMMENDATIONS
For the future work recommendation are add extra method such as ARIMA for
comparison, add the element of design in terms of new system that can be applied to the
building either in electrical or mechanical, add another factor that contribute to energy
consumption and implementation of solar PV system to the building
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24. APPENDIX