AN ANALYSIS OF ENERGY EFFICIENCY BY APPLYING ENERGY

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:

INTRODUCTION

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

Why ENERGY EFFICIENCY?Electricity Supply Act 1990Efficient Management of Electrical Energy Regulations 2008

Section 3 (1A)- Any installation which receives electrical energy from a licensee or supply authority witha total electricity consumption equal to or exceeding 3,000,000 kWh as measured at onemetering point or more over any period of six consecutive months that will conducted byREEM

PLO Cover: PLO 2, PLO 12, PLO 1

0

2,000

4,000

6,000

8,000

10,000

12,000Electricity 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

GW

h

Year

Energy Demand: BAU vs NEEAP

BAU

NEEAP

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


https://www.tnb.com.my/commercial-industrial/pricing-tariffs1

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


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


LITERATURE REVIEW

• 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

PLO Cover: PLO 2,PLO1,PLO 11

METHODOLOGY

Research FrameworkAudit 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

ResultsPLO Cover: PLO 12,PLO 5, PLO1

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 Lighting2.0Indoor Lighting

PLO Cover: PLO 12,PLO 5, PLO1

Variable Parameter


Classification of Energy Efficiency Investment Through Energy Performance Contract (EPC)

1NO/LOW COST

EASY

3HIGH COST

EASY

2NO/LOW COST

HARD

4HIGH COST

HARD


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

LightingSkywarth building

(1) 24 Hours Mobile Workstation



(3) Use of Occupancy Sensor

Skywarth and Skymage building(3) Energy Awareness Campaign


(2) Tariff management


(3) LightingRetrofitting



Based on biological brain

Multilayer Perceptron methodRadial Basic Function method

Data processing occurs at neurons or nodes

Output a single based on the input signal

Artificial Neural Network


RESULTS AND

DISCUSSION

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

ELECTRICITYCONSUMPTIONLinear Regression

MLP

RBF

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

Jan

-13

Mar

-13

May

-13

Jul-

13

Sep

-13

No

v-1

3

Jan

-14

Mar

-14

May

-14

Jul-

14

Sep

-14

No

v-1

4

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

PLO Cover: PLO 4, PLO 5

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)

PLO Cover: PLO 4, PLO 5

Cost Saving And BEI


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




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




Annual Electricity Consumption (MLP)(kWh) 8,815,588.00




Potential Saving (RM) 147,388.00

Energy Efficiency Retrofitting Approach Investment

(RM) 499,539.00

Return of Investment (Years) 3 Years

CONCLUSION

AND

RECOMMENDATIONS

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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APPENDIX

AN ANALYSIS OF ENERGY EFFICIENCY BY APPLYING ENERGY

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