CHAPTER 1

INTRODUCTION

1.1 Overview

Buildings consume almost the highest energy consumption in Malaysia, and

the highest energy that been consumed by the buildings is the electricity especially

office buildings which use lots of energy (Zeeshan, 2014). Due to some policy and

standard, nowadays every office buildings is required to minimize the energy usage

but at the same time need to maintain the comfort zone of the occupants. In order to

achieve this objective, some methods have been proposed such as implementation

of renewable energy in the building, building energy efficiency, thermal energy

storages and others (Zainordin et al., 2012).

A well designed energy efficiency building maintain the best environment for

human habitation while minimizing the cost of energy. This type of building usually

have a higher energy efficiency as the objective design of the building itself is to save

energy in terms of electricity, water and cost. Some of the designs that been highlight

in this type of buildings are the design of the façade which is focusing on the light

distribution and heat transfer during daylight in term of thermal comfort (Zainordin et

al., 2012).

For some building which is not being designed with those energy efficiency,

this retrofitted building optimize their energy consumption by other method such as

thermal energy storage, Energy management system (BEM), and energy efficiency

analysis. All of these methods were apply in terms of cooling, heating, lighting, pump,

1

chiller and more. The advantage of this system is able to save energy and increase

the thermal comfort for the consumers. Malaysia’s standard also plays an important

role as it require the others to follow the specification of the feasibility of energy

efficiency design standards in order to reduce the energy consumption in Malaysia

as energy is not a fixed cost and it may be one of the most controllable inputs

(MAESCO).

1.2 Research Background

The demand for energy in developing countries is becoming alarming,

whereas the means of electricity production remains limited. Due to climatic change

high temperature and humidity, significantly increases the use of air conditioners to

attain better thermal comfort (Zeeshan, 2014). Depletion of fossil fuels and increase

in fuel cost leads to an electricity shortage. The energy crisis made an effort in

reducing the overall energy consumption in building sector. Energy is a vital input for

social and economic development. As a results of the generalization of agricultural,

industrial and domestic activities, the demand for energy has increase remarkably,

especially in emergent countries. Despite the obvious advantages of renewable

energy, it presents important drawbacks, such as the discontinuity of generation, as

most renewable energy resources depend on the climate, which is why their use

requires complex design, planning and control optimization methods.

In this analysis, the case study is to analyze the energy efficiency of the office

building located in Klang Valley. Energy efficiency is a method where the process is

to maintain or reduce the amount of energy usage that been used to produce a

better output (MAESCO). The objective of the energy efficiency is to use lesser

energy to accomplish the same tasks, in addition to enjoy the same comfort level as

before applying the energy efficiency. By analyzing the energy efficiency in the

building, the analysis will come out with the data to determine which systems or

applications that is consuming the highest electricity. The data will be used as an

input and by using neural network, the prediction of the energy to be used can be

2

obtained. By referring to the prediction of the energy and optimization of the system,

we found that the energy can be reduced, thus the cost of the electricity is

decreasing.

In the office building there are several loads and applications that consume

most of the energy such as heat ventilation air conditioning system (HVAC), lighting

system, office appliances and others. Among the three largest energy consumption,

HVAC system contributes the highest in the energy consumption (Zainordin et al.,

2012). This is because of the equipment such as chiller, air handling unit (AHU),

boilers, pump and supply fans which are using a large number of electricity in order

to operate it. Plus, most of this equipment operate at a longer time compare to the

others. For this analysis, the methods that will be use is the multilayer perceptron

artificial neural network and the monitoring and targeting process for the energy

efficiency so that can plan strategy aimed for the energy efficiency improvement

(MAESCO).

1.3 Problem Statements

The increasing of energy consumption in Malaysia and depletion of resources

had led to several researches on the energy efficiency in building especially office

building where it contributes to the largest energy consumption compared to the

other buildings (Zainordin et al., 2012). The reasons for this matter are because of

the rising of demand for the service provided by building and increasing level of

human comfort along with the increase of time spent in building assure that in future

this upward trend in energy demand will continue. The increasing of electricity tariff

where 29.6 sen/kWh for the first 200kWh and 37.2 sen/kWh for the next kWh which

is due to economic factors also contribute to the analysis of the energy efficiency in

the building as the owner of the building has to pay high amount of money for the

energy consumption. Some equipment or factors have the potential to be improved

and saved where it is related to this study which is to decrease or maintain the

amount of the energy consumption without decreasing the level of the human

comfort in the building itself (Tenaga Nasional Berhad, 2014). Besides, the

3

increasing of carbon emissions is one of the reason for an energy efficiency analysis,

where building is releasing a certain amount of carbon which is cause by the big

equipment such as pump, motor and chiller that is located in the building itself. Other

than that, lack of monitoring on the building energy management where lead to

inefficiency energy management also will cause the increasing of energy

consumption without a proper control (MAESCO).

1.4 Objectives

The objectives of this analysis are:

To identify the highest energy consumption systems/applications in the

building

To investigate potential energy efficiency approach in commercial building

To analyze and mitigate the energy efficiency in Klang Valley building by

using artificial neural network

To provide a guideline for industry in improving the energy consumption

and cost saving

1.5 Scope of Study

The scope of the study is focusing in the high rise office building located in the

Klang Valley area which is Skywarth building (33 storey) and Skymage building (44

storey). The reason for choosing the high rise buildings is because when applying

the energy efficiency, it will show more effect on the high rise building compared to

the small building. For the method, this research will only focus on the prediction by

artificial neural network using SPSS Statistics software.

4

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter will discuss about reviews of existing projects and researches in

the project design, conception and any information that relates to the improvement of

this research. This chapter will also cover background of the study and method that

had been used in previous work and current work.

2.2 General Information on Energy Efficiency in Office Building

Nowadays, energy efficiency has become an important aspect in order to

maintain the best environment for human habitation while minimizing the cost of

energy, and for this report it is focusing on the office building. The energy efficiency

for building consists of improving the comfort levels of the building occupants and

reduce energy use (electricity, natural gas, etc.) for heating, cooling and lighting.

Energy efficiency factors in buildings vary according to geography, climate,

building type and location. The distinction between developed and developing

countries is important, as it is the contrast between retrofitting existing buildings and

new construction. In all cases, there are different standards of building quality. It is

5

vital that energy efficiency permeates all levels and is not restricted to high end

properties. For some countries that have four seasons climate change, the site

energy demand will increasing as people seek to maintain comfort levels in more

extreme conditions. The other main drivers are demographics, economic,

development lifestyle, changes technology and the spread of new equipment

(Zeeshan, 2014).

In Malaysia, the major energy usage in office is air conditioning (57%),

followed by lighting (19%), lifts and pumps (18%) and other equipment (6%). The

energy consumption in buildings is in terms of the Building Energy Index (BEI).

According to study done by Nadzirah Zainodin from IMPERIA Institute of

Technology, the average of BEI for Malaysia and Singapore is 269kWh/m2/ yr and

230kWh/m2/ yr respectively compared to South East average BEI which is 233kWh/

m2/ yr. In 1989, the Malaysian Ministry of Energy, Water and Communication

(MEWC) had introduced the Guidelines for Energy Efficiency in Non-Domestic

Building. Later, the guidelines were renamed and revised as the Malaysian Standard

MS 1525:2007. The aim for this standard was to encourage the application of

renewable energy sources, pollution and energy consumption while maintaining

comfort, health and safety of the building occupants (Zainordin et al., 2012).

2.3 Previous Researches

Based on the previous work that have been done by Sadaf Zeeshan in

January 2014, where the study is focused on the major part of energy utilization

which is HVAC system, lighting system and office appliances. The method that has

been used in this case study is by using a software based on CLTD/CLF (cooling

load temperature difference/cooling load factor). These parameters then optimized

according to the building requirements. The process of the case study is almost the

same with this study which started with collection of all relevant building data such as

architectural drawing, building location, climate, air flow rate, lighting, office

equipment used, temperature, and most important is the electricity consumption

through electricity bills for a whole year. In the case study, the building was divided

6

into 11 floor areas which is sales office, waiting room, store room, reception, meeting

room, manager offices, management hall, two offices, office, buying office, meeting

room and reception. The reason for division of the zones are to analyze the prime

areas consuming maximum energy where the potential for energy conservation

exists. This case study also has a similarity as the bill of the electricity and electricity

consumption is evaluate according to each month in order to determine the trend of

the system.

Another similar work is from a research study on energy analysis of a building

using artificial neural network (ANN), done by Kumar and Aggarwal in August 2012.

The similarity of this energy analysis with the present work is the method that been

used in this analysis is the same which is artificial neural network. The only different

is the present work will only focusing on Back-Propagation ANN in order to predict

the energy consumption in the building office in Klang Valley, compared to the

previous work where it is done by using more than BP ANN method which is

including recurrent neural networks, auto associative neural network and general

regression neural network. In the previous work the ANN is use to predict the heating

and cooling load, indoor air temperature, HVAC system and prediction of energy.

Other than that, another similar work is from a research study on the

comparison between traditional Neural Network and Radial Basic Function network

by Tiantian Xie et al. This research is close to this current but the different is in the

current work, the ANN method is compared with Linear Regression. In this research,

the purpose is more on the study between the structure of the MLP and RBF itself.

The data set for both methods were compared on four problems which is forward

kinematics, peak function approximation, two spiral problem and character image

recognition. The recommendation that can be considered for this research is to make

the comparison of the ANN method with the other non –linear system based on the

same problem

A research by Mehreen and Sandhya which is on understanding the energy

consumption and occupancy of a multi-purpose academic building. This research is

one of the researches that been referred in order to study the factors of energy

consumption especially the factor that caused by the occupancy. In this research, it

stated that commercial building, primarily office and university building are classified

7

amongst the building presenting highest energy consumption. The case study of this

research is in the university building as the factor of occupancy will contribute the

biggest in energy consumption factor. This research divided study in different area in

the university building itself in order to determine the highest energy consumption

based on the area. This research also conducted a survey in order to determine the

place where the students mostly use the highest electricity. The recommendation

that can be added in this research is to study more on the other factors that also

effect the energy consumption in the university building such as in term of

machinery, cooling and heating degree day.

Lastly, the research which conducted by Nurhidayah and Rahaizat from

Universiti Technologi Malaysia which is on the utility consumption among Malaysian

electricity users in government buildings. This study is focusing more on the behavior

of the consumer toward energy consumption. This study research on the five

different aspects which are awareness, electricity consumption, environmental

concern, micro environmental behavior and macro environmental behavior. As a

suggestion for this research is to consider other factors that also involving with the

behavior of the end users.

2.4 Present Work

For the energy efficiency in current work, it is focusing on the office building

which is located in Klang Valley. The analysis is focusing more on the pattern of the

data that will be collected in order to study the pattern of the system itself. The

important of the system pattern is to identify the equipment that consume largest

energy consumption, floor area which is using the highest energy and to identify

certain particular events during the increasing and decreasing of the energy

consumption based on the pattern that obtain by analysis of the data.

In order to analyze the pattern of the system, a selection of data is collected

and by using the data a proper baseline which is consisting 12 months of data is set

as a benchmark in order to analyze the system pattern. Then a target is set base on

8

the baseline, where this process is more on to prediction of the energy consumption

to determine the amount energy that will be saved and reduced or maintain without

effecting the comfort level of the occupants. The method used for prediction of the

energy consumption will be using artificial neural network as this method has a less

error compared to the other methods (Ahmad et al., 2014)

This research also identify the potential of saving method that can be

implement in both Skywarth and Skymage building. Other than that, this research

also provide the cost saving for all the three method based on the error square of the

method. Table 2.1 show the summary of previous works compared to the present

work. Further explanations on the methodology used in this study will be discussed

in the next chapter

Table 2.1 Comparison of previous works with the present work

Decisive Factor Present work Previous workMethodology Present work is more on

two methods which is

multilayer perceptron

artificial neural network

(ANN) and implementation

of the solar system in the

office building. The ANN

will be used when

targeting or prediction for

the energy consumption

based on the data that we

collected, while solar

system is the energy

efficiency method that will

be implemented in order to

achieve the amount of

energy that had been

targeted using the ANN

method earlier.

In previous work, the

method were not specific

into one method as it is

using multiple of ANN

method including BP and

the results from the

previous works were

compared to each method

to determine the accuracy

of the method itself. Plus

there is no comparison

among the ANN method in

order to determine the

accuracy of the method

itself

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Indicator Indicator for the present

work is the data that had

been collected based on

the office building at the

Klang Valley area. Then

the data for floor area also

been collected which is

different from previous

work which only take the

overall data for the whole

building. The data is based

on measurement and also

the data from the

company. The data that

been consider is only

temperature and kWh

Data that had been

collected is for overall

office building but the

different is the data is to

identify the equipment

which is contribute to the

most energy consumption

in the office building. Other

previous works also show

multiple data of different

building for comparison

purpose.

Sample of countries Sample country is

Malaysia and specific

place is Klang Valley area.

Most of the previous work

located in india and

Pakistan which is for

country that has four

seasons in a year.

2.5 Summary

From the previous works, it is clearly stated that the ANN method is more

accurate compare to other methods such as regression method and ARIMA. Some

of the previous research had already proven that ANN prediction is close to exact

valued (Kumar and Aggarwal, 2013). This research also include a comparison

between the two ANN methods which is Multilayer Perceptron and Radial Basic

Function before comparing with the traditional method, Linear Regression.

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CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

This chapter discussed the method that will be used in the energy efficiency

analysis which is Artificial Neural Network. This chapter also explained on the

research framework that consists of all the methods used in this study in order to

monitor and implement the energy efficiency on the building. The discussion starts

with the factors that affect the energy consumption. The data that had been collected

will be analyzed using artificial neural network and it will be compared with linear

regression method and to determine the accuracy of the method.

3.2 Factors of Energy Consumption in the Building

In Malaysia, commercial building consume almost the highest energy which is

electricity especially for the commercial office building (High rise). Based on the

Electricity Supply Act 1990 under Efficient Management of Electrical Energy

Regulations 2008 section 1(1A) states that any installation which receives electrical

energy from a licensee or supply authority with total electricity consumption equal to

or exceeding 3MWh as measured at one metering point or more over any period of

six consecutive months were require to conducted an energy audit by a authorize

individual known as Register Electrical Energy Manager or REEM (Energy

11

Commission). The energy consumption in the office or commercial building is due to

some major factors within the building itself and also the environment surrounding it.

In this research the only factor that is being considered for the prediction is the

temperature as the related data, for the others may difficult to get and some of the

factors only have a small effects toward the energy consumption because of the type

of the building for this research which is commercial building.

Equipment have an important role for the energy consumption as it is divided

into two parts. The first part is the number of equipment that have been used in both

Skywarth and Skymage buildings itself. The equipment that been identified as major

energy consumption are chiller, water pump, fan, motor and mostly other heavy

mechanical equipment. This type of equipment consume a large amount of energy

as most of the equipment consist of motor and also heating or cooling process which

have a large horsepower usually the highest energy contribution in the building

(Detlef, 1999). As the number of equipment increases, it also increases the number

of loads which then increase the consumption of electricity in order to operate the big

load in the buildings. The second part of equipment is the efficiency of the

equipment. An equipment that has a poor efficiency will consume more electricity,

compared to equipment that have a higher efficiency that will consume less power

but able to carry out the same amount of work (Detlef, 1999). At the earlier stage, the

investment for a high efficiency equipment is very high compared to the less efficient

equipment, but in term of long period the high efficiency equipment will be more

benefit to the owner. In energy efficiency, the focus is more on reducing the real

power (kw) which is dissipated the resistive component that performing the “work” in

a system (Energy Savings Analysis, 2012). By reducing the energy use for the

resistive components in the system to perform the same amount of work, the system

will be more efficient and more saving.

Temperature is the only factor that been considered for the prediction in this

research as the influence of temperature is very crucial especially in Malaysia. Even

though Malaysia is not a four season country, but cooling as thermal comfort is a

very demanding among the Malaysian. In this research the temperature that has

been taken is the cooling degree day at the Klang Valley area for both buildings. A

cooling degree day are used during warm weather to estimate the energy needed to

cool indoor air to a comfortable temperature. Mean daily temperature is converted to

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cooling degree-days by subtracting the reference temperature from the mean. For

example, a day with a mean temperature of 80F and a reference temperature of 65F

would correspond to (80-65), or 15 cooling degree-days. Higher values indicate

warm weather and results in a high power production for cooling (Chua, 2010).

Meanwhile heating degree days are determined by subtracting the mean

temperature for the day from the reference temperature. Thus, if the mean

temperature for a day is 50F and the reference temperature is 65F, there would be

15(65-50) heating degree-days on this day. On the days when the mean temperature

is above the reference temperature, there are no heating degree-days. Therefore,

the lower the average daily temperature, the more heating degree-days and the

greater the consumption of fuel. But in this research, the heat degree day were not

taking into consideration because Malaysia is a tropical climate country with climate

categorized as equatorial which is hot and humid throughout the year. The carbon

emission also play an important role in energy consumption as the earth atmosphere

layer acts like a blanket, keeping the earth warm and shielding it from the cold of

universe. This is commonly referred to as the greenhouse effect. Carbon dioxide

(CO2), which most potent greenhouse gas, is nevertheless the main influence of the

greenhouse effect (Chua, 2010). When fossil fuels such as coal, oil and natural gas

are burnt they release CO2 into the atmosphere. Because of this, the layer of

greenhouse gas is getting thicker, which is in turn making the Earth warmer. In term

of electricity every 1kWh release 0.747gram of carbon dioxide which is crucial to the

environment (SEDA). Due to the increasing of the carbon emission it lead to climate

changes which cause the earth getting hotter that before. As the results the user in

any facility or building may double the electricity consumption in order to fulfill their

comfort level in term of cooling the building.

People also among the factor that lead to the increasing or reducing of energy

consumption in the building. The reason for this fact is as the number of occupancy

increase in a certain building the demand of the usage will also be increase which

mean the need of the electricity will be more compared to the normal condition. But

in this research the factor of people were not consider as it is less effect on the

commercial building that usually have a fix amount of people in the building. The

increasing rate of the occupancy in the commercial building is very low. Plus the

case study building is a building that is fully occupied. In Commercial building this

13

factors will affect the energy consumption only at the earlier stage of the building

occupancy as the number of occupant in the building will increase until a certain of

period where it will be fix (Nurhidayah, 2014). This factor will be consider if the type

of the building is hospital, school, university and other building that have an unfixed

number of occupancy (Nurhidayah, 2014).

Operating schedule in a building is controlled by the Building Control System

(BCS) to operate. If the operating of the equipment of the building is on at the same

time during morning it will increase the maximum demand rapidly. Instead of turn on

all the equipment at the same time it will be more efficient if the start-up operation of

the equipment have a delay between each equipment (Hassan, 2014). By using this

technique the building can reduce it maximum demand which is also lead to reduce

the energy that been used during the start-up of the building. Beside that this

technique will also reduce the electricity bill as the utility supply will charge on the

highest maximum demand in their tariff calculation as TNB need to cater for this

peak load whenever required by the customer. Since electricity cannot be stored

there must be sufficient available generation, transmission and distribution capacity

to meet the highest demand (Tenaga Nasional Berhad). Other than that, the

extending operating hour will also increase the energy consumption more compared

to the normal operation of the building. The on-peak and off-peak tariff also play an

important factor for the energy consumption as the on-peak tariff will be more

expensive compare to off-peak tariff. Sometimes the amount of energy that been

consumed is the same but due the tariff and the operating schedule the bill getting

higher as the time to operate in not been organize well.

Facade is the last factor that effect the energy consumption. The floor area in

a building will affect the energy consumption by the amount of power that will be

used in order to cooling or the usage that amount of area. The bigger the floor area

the more energy will be used to fulfill the need it (Zeeshan, 2014). But this factor is

also related to the people factor as the occupancy in the building. Even if the area if

big enough but the occupancy in the building is less that there will be no need to

supply electricity at the non-occupant area. The other factor in term of building

structure is the façade design of the building itself. This factor has been considered

during the process of design the building on the earlier stage. For example the

window shading can effect energy consumption if the sunlight that gets through, the

14

window are not penetrate and the heat were not minimize the user in the building,

hence it will need a lot more electricity in order to fulfill they comfort level as the heat

from the sun were not minimize properly (Zainordin et al., 2012).

Figure 3.1 Energy consumption factors

15

3.3 Research Framework

The first step for this analysis is the collection of all relevant building data that

related to the building energy consumption. The data that has been collected

including the architectural drawing, building location and electricity consumption

through electricity bills for a whole year which is 12 months in order to develop the

baseline so a prediction can be made by using artificial neural network (ANN)

method. After the data collection, all the data will be analyzed to study the trend of

the system. The reason for this matter is to determine the reason for the system

increase and decrease in energy consumption in the building. By determine the trend

of the system, it is easier for the researcher to study on the system characteristic and

the factors that will affect the system energy consumption. The frame work for the

analysis is shown as in Figure 3.1 below:

Figure 3.2 Research framework

16

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

Audit energy consumption in the building and problem analysis

Results

Based on this framework, this research is organized by audit energy

consumption in the building and problem analysis where the problem that had been

stated in the thesis which the increasing of carbon emission, the increasing of utility

tariff, building as the highest contributor to energy consumption in Malaysia and the

lack of awareness in monitoring the building energy management. In order to get the

initial results, the two buildings system need to be studied to understand the factors

that influenced the energy consumption on study of the case study for this research.

All the relevant factors where then transformed into an Ishikawa diagram or known

as fish bone diagram in order to separate different factors that contribute to the

energy consumption in the building. The energy accounting is also develop based on

the audit for both building in order to determine the highest energy consumption for

both building exclude the cooling water.

The research will then continue by monitoring, targeting and compiling the

data that had been obtained from the audit. All the data are compiled but the data

that been considered in this research are only the kWh and the temperature in the

Klang Valley area only.

Once the data had been compiled, the next stage is to perform an energy

analysis by using the energy efficiency approach that can be applied to the two

buildings in order to reduce the energy consumption. For the energy efficiency

approach, this research focus on the highest contribution in the system which is the

lighting system. So, the energy efficiency that been selected is the lighting

retrofitting. The impact of the retrofitting toward the active power consumption is

based on the saving that can be obtained.

The data of the factor and the kWh is use to predict the energy consumption

in order to find the most accurate and less error method to develop a baseline for the

industry guideline. The prediction is based on two Artificial Neural Network method

which is Multilayer Perceptron and also Radial Basic Function. The prediction from

the ANN will also be compared with the prediction by using Linear Regression

method as it is the traditional method that been used in the industry. The results will

show the most accurate method and also the cost saving that can be predicted by

applying the ANN method.

17

3.4 Data Collection

The data collection of the research is based on the measurement and the data

that had been obtained from the industry (Green3 Sdn Bhd). The data obtained from

the company are the data that based on the Kilowatt hour for two building which is

Skywarth and Skymage building. These data also include the floor area of the two

buildings. Half of the data for the energy accounting were obtain by using

measurement at the site and the rest of the data that obtained by the company were

based on their measurement.

3.5 Data and Cost Saving Analysis

Based on the prediction from the three methods that had been used, a cost

saving analysis is developed in order to determine the potential of cost saving based

on the most accurate method. Basically the most accurate method will produce a bit

low in the saving as the results from the prediction is close to the actual where it will

follow the trend of the actual energy consumption in the two building. That is the

reason why the results from the root mean square error will also be compared with

the amount of saving in order to determine the relevant of the value that obtain from

the cost saving measurement. The less error method will determine the most

accurate cost saving values.

3.6 Energy Consumption Information

The selected buildings, Skywarth and Skymage building consist of two types

of energy supply which are electricity supply and also thermal energy (chilled water)

for each building. The electricity provider is Tenaga Nasional Berhad (TNB) while the

chilled water is supplied by Gas District Cooling Sdn Bhd (GDC). This cause the

energy supply to the building is spilt into two part. According to the energy audit both

18

buildings have obtained 100% energy consumption as shown in table 3.1 and table

3.2 below:

Table 3.1 Summary of Annual Utility Payment for Building Skywarth

Utility Annual Payment PercentageElectricity RM 2,648,494.76 45.25%

Chilled Water RM 3,204,212.71 54.75%

RM 5,852,707.47 100%

Table 3.2 Summary of Annual Utility Payment for Building Skymage

Utility Annual Payment PercentageElectricity RM 3,542,718.00 43.3%

Chilled Water RM 4,647,766.00 56.7%

RM 8,190,484.00 100%

Due to the objective of the research which is to analyze the trend of electricity

consumption in the commercial building, the data for the chilled water supply by the

GDC is not taking into consideration because the data for the chilled water are not

available. Plus the objective also is to prove the reliability and accuracy of the

selected method for the baseline prediction which is Artificial Neural Network. Thus,

the consideration for the method only focus on the electricity (kWh) supply by the

TNB.

The electricity supply rate based on the TNB C1 tariff commercial government

building for both building are shown in Table 3.3 below:

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Table 3.3 Electricity Supply Rate for Buildings Skywarth and Skymage

Definition Rate Remark

Unit Consumption (RM/kWh)

0.365 Taxable

Maximum Demand Rate (RM/kWh)

30.30 Taxable

Renewable Energy (RE) Levi

1.6% on total bill Taxable

Connected Load Charge, CLC (RM/kW)

RM8.50 per kW of

demand lesser than the

declared Maximum

Demand.

Chargeable from Jan 2015

onwards.

Imbalance Cost PassedThrough (ICPT)

Rebate RM0.0225 per

kWh

Non-taxable

From 1st March to

31st December 2015

Goods and Service Tax(GST)

6% on total bill Effective April 2015

In order to study the process of the system in the building, an energy

accounting must be determined for both Skywarth and Skymage building. Energy

accounting is a management technique that used to monitor energy consumption

and also relates consumption to key independent variable such as production and

weather, and assesses energy performance or efficiency over time and against

relevant benchmarks. The successful practice of energy accounting is predicated on

the identification of the right kinds of data to be collected, the use of appropriate

statistical methods to correlate consumption to the independent variables, and the

reporting of the right information to the right people in the organization. Energy

Monitoring and Targeting (M&T) is a technique for energy performance analysis that

overcomes possible deficiencies in the traditional performance indices or energy

intensity. Based on the energy accounting then the energy consumption capacity of a

building can be determine in order to consider an energy efficiency toward the target

systems in the building.

20

According to the energy audit that had been conducted before, there are

several measurement that be consider in order to come out with the full analysis to

determine which part of the building contribute most to the energy. The

measurement of the energy accounting includes the number of lamp indoor and

outdoor, the air handling unit (AHU) reading, the number of lux, carbon dioxide

measurement and others measurement that related to electricity consumption. The

details energy accounting for electricity consumption for both buildings are shown

below without consider the chilled water data:

8.1; 8%6; 6%

2.1; 2%1.5; 2%0.5; 1%1.3; 1%0.1; 0%

37%

24.4; 24%

18.6; 19%

Skywarth Building Energy Accounting

Office Equipment & Small PowerChilled Water PumpsElavatorsAir Cooled SplitMechanical PumpFan Coil UnitCompound and Façade LightingIndoor LightingAir Handling UnitMechanical Ventilation

Figure 3.3 Annual Electricity Accounting Building Skywarth

21

Table 3.4 Detail Annual Electricity Accounting Building Skywarth

Building Services Annual Energy Consumption (kWh)

Percentage

Indoor Lighting 2,108,177.9 37.4%

Air Handling Unit 1,376,106.5 2

4.4%

Mechanical Ventilation 1,047,418.3 1

8.6%

Office Equipment & Small Power 459,491.3 8.1%

Chilled Water Pumps 337,719.05 6.0%

Elevators 116,900.0 2.1%

Air Cooled Split Unit 86,023.2 1.5%

Fan Coil Unit 72,207.2 1.3%

Mechanical Pump 27,156.0 0.5%

Compound and Façade Lighting 8,394.7 0.1%

Total Electrical Energy Consumption 5,639,594.0 100.0%

3.2; 3%11.6; 12%

1.1; 1%2.6; 3%3.4; 3%

2; 2%

34.4%

16; 16%

10.7; 11%

15.1; 15%

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

Air Handling Unit

Mechanical Ventilation

Office Equipment & Small Power

Figure 3.4 Annual Electricity Accounting Building Skymage22

Table 3.5 Detail Annual Electricity Accounting Building Skymage

Building Services Annual Energy Consumption (kWh)

Percentage

Indoor Lighting 2,590,421.7 34.4%

Air Handling Unit 1,202,379.8 16.0%

Mechanical Ventilation 805,146.2 10.7%

Office Equipment & Small Power

1,140,783.5 15.1%

Chilled Water Pumps 242,262.75 3.2%

Elevators 871,576.2 11.6%

Air Cooled Split Unit 79,540.8 1.1%

Fan Coil Unit 194,824.6 2.6%

Mechanical Pump 257,477.4 3.4%

Compound and Façade Lighting

148,482.0 2.0%

Total Electrical Energy Consumption

7,532,895.0 100.0%

3.7 Energy Efficiency in High Rise Building

Energy efficiency is a process towards reducing the energy consumption in

any area or facility without effecting the comfort level of the end user. After an energy

audit, there are 9 recommendations for the lot Skywarth building and 7

recommendations for the lot Skymage for further improvement in term of energy

consumption which is mean the next step after energy audit. From the previous data,

the most facility that consume higher electricity in the commercial building excluding

chilled water is the lighting and the air handling unit. Based on the energy audit

energy cost saving measurement (ESMs), there are several potential area had been

identified in order to reduce the energy consumption without effect the end user

23

comfort and the energy cost saving measurement (ESMs) in this research is specific

to reduce the energy consume by the highest energy contributor which is the lighting

system. The classification of the energy efficiency approach is shown in Table 3.6.

Table 3.6 Energy Efficiency Approach Classification

1

No / Low Cost

Easy

3

Medium Cost / High Cost

Easy

2

No / Low Cost

Hard

4

Medium Cost / High Cost

Hard

24

3.7.1 Potential Energy Efficiency Approach in Lot Skywarth

A total of 9 Energy Cost Saving Measures (ESMs) were identified, as listed in

Table 3.6 and the ESMs are divide into three categories as shown below:

Table 3.7 Energy Cost Saving Measurement

No. Method Category1 Tariff Management: Apply for "Off Peak

Tariff Rider"

Low cost

Tariff Management

2 Control the Maximum Demand -

Implement Maximum Demand Control

Strategy (Manual Control)

No cost

Tariff Management

3 De-lamp in a selected area No Cost

4 Retrofit Normal Fluorescent Tube (36 W),

Compact Fluorescent Downlight (18 & 26

W) and 50-W Halogen with equivalent

LED.

Medium - High Cost

5 Reschedule operation hour for lighting No Cost

6 Install Occupancy Sensors turn off

approximately 50% of lamps when the area

is not occupied.

Medium - High Cost

8 Run Energy Awareness Program to ensure

all Office Equipment and Lighting are

turned off by end users.

Low Cost

9 Create 24 hours Mobile Workstation to

reduce lighting & cooling energy required to

run equipment after hours

No Cost

25

3.7.2 Potential Energy Efficiency Approach in Lot Skymage

A total of 8 Energy Cost Saving Measures (ESMs) were identified at the lot

Skywarth which listed in Table 3.7 below:

Table 3.8 Energy Cost Saving Measurement

No. Method Category1 Tariff Management: Apply for "Off Peak

Tariff Rider"

Tariff Management

2 Control the Maximum Demand -

Implement Maximum Demand Control

Strategy (Manual Control)

Tariff Management

3 De-lamp in a selected area No Cost

4 Retrofit Normal Fluorescent Tube (36 W),

Compact Fluorescent Downlight (18 & 26

W) and 50-W Halogen with equivalent

LED.

Medium - High Cost

5 Install Occupancy Sensors turn off

approximately 50% of lamps when the area

is not occupied.

Medium - High Cost

6 Run Energy Awareness Program to

ensure all Office Equipment and Lighting

are turned off by end users.

Low Cost

7 Create 24 hours Mobile Workstation to

reduce lighting & cooling energy required

to run equipment after hours

No Cost

26

3.8 Prediction by Using Artificial Neural Network

Artificial neural network is the term that used to describe a computer model

assumption of the biological brain. It consists of a set of interconnected simple

processing units (neurons and nodes) which combine to output a signal to solve a

certain problem based on the input signal it received. The interconnected simple

processing have an adjustable gains that is slowly adjusted through iterations

influenced by the input-output patterns given to the ANN. An ANN is an information

processing system that has certain performance characteristic in common with

biological neural networks. Basically ANN is a system that handles many input

signals. We need to process and use the output in order to solve a task that it has

been trained to solve,. Example of ANN structure is shown in Figure 3.5 below:

Figure 3.5 ANN structure

ANN has been developed as a generalizations of mathematical models of

human cognition or neural biology, based on the assumptions that information

processing occurs at many simple element called neurons or nodes. The signals are

passing over connection links and each connection link has an associated weight,

which in a typical ANN, multiplies the signal transmitted. Each neuron applies an

activation function which is usually non-linear to its net input to determine its output

signal. A learning rule exist to adapt the weight to solve a particular task such as

engineering business and others.

27

Artificial neural networks are the most widely used for implemented methods

in forecasting the building energy consumption. The ANN also used in finance and

manufacturing for the same reason which is implemented methods that has been

used to predict the production and budget. Before using ANN in the analysis of the

energy efficiency, there are some advantages and disadvantages that must be

considered as shown in Table 3.8 below:

Table 3.9 Advantages and disadvantages of the ANN (Ahmad et al., 2014)

Advantages DisadvantagesA ANN can execute the task that a linear

program cannot

The ANN need training to operate

ANN can be executed in any applications The ANN needs to be emulated since the

architecture of neural network is different

from the architecture of microprocessor

ANN are parallel in nature so when an

element of the ANN fails, it can continue

without any problem’s

For a large ANN, a high processing time

is required

As the complexity of building energy system is very high so the ability of the

ANN in performing the non-linear analysis is an advantage in prediction for the

energy consumption. Compare to the other statistical method such as SAS, SPSS,

GENST and MATLAB, due to the difficulty of the calculations, the probability of error

are even higher as this method are based on conventional algorithms such as the

least square method, moving average, time series and curving fitting. Plus the

performances of this algorithms are not robust enough especially when the data set

becomes very large.

ANN is much better than the statistical method as it has been developed as

generalizations of mathematical models of biological nervous system. The ANN has

successfully passed the research stage and many real applications in various fields

such as engineering, mathematics, medicine, neurology automotive, financing,

manufacturing, and many others. ANN is a group of simple elements that operating

in parallel. The results of the ANN depends on the number of hidden layer neurons.

In order to get an optimize results, the selection of the number of hidden layer

28

neurons should be optimized. There are several types of ANN such as back

propagation (BP), real coded genetic algorithm (RGA), self-organizing map (SOM)

and others. Many of the building energy systems are exactly the types of problems

and issues for which the ANN approach appear to be most applicable. The ANN has

the ability and potential for making better, faster and more practical predictions

compared to the others traditional methods. In this research the software that use to

simulate the Artificial Neural Network is the SPSS Statistic. SPSS Statistics for

Artificial Neural Network offers a non-linear data modeling procedures that will

enable the user to discover more complex relationship in the data that been simulate.

SPSS Statistics also allow the users to discover more complex relationships between

the selected data. It also allow the users to set the conditions under which the

network learn, which is in this case Multilayer Perceptron and also Radial Basic

Function network.

By using this software the users is capable to control the training stopping

rules and the network architecture, or let the procedure automatically choose the

architecture for the users. As the start, the selection of network for ANN is choose

either MLP or RBF algorithms to map the relationships implied by the data. This

simulation is based on the feed forward architecture which move data in only one

direction, from the input nodes through the hidden layer or layer of nodes to the

output nodes. The algorithms that operate on a training set of a data and then apply

that knowledge to the entire data set and to any new data. The variables must be

specify and which may be scale, categorical or a combination of the two. By

adjusting each procedure by choosing how to partition the data set, which

architecture to use and what computation resources to apply to the analysis. The

results that is obtain can be choose whether to display the results in tables or

graphs, save optional temporary variables to the active data set, or export models in

XML-based file format to score future data. Also by using this software the missing

value that obtain from the results can be found by using several technique such as

series mean technique, mean of nearby points and median of nearby point.

29

3.8.1 Classification of Artificial Neural Network

A.) Multilayer Perceptron

The method has been employed for energy efficiency method is the multilayer

perceptron which is a feedforward artificial neural network model that maps sets of

input data onto a set of outputs where in our case is the prediction of energy. The

reason for this selection is because multilayer perceptron is one of the most powerful

learning algorithms in artificial neural network, the structure of the multilayer

perceptron are shown in Figure 3.6 below:

Figure 3.6 Multilayer perceptron structure

The Back-Propagation is a multi-stage dynamic system optimization method of

training artificial neural network so as to minimize the objective function. It requires a

dataset of the desired output for many inputs, making up the training set. It is most

useful for the feed forward network which a network that have no feedback, or

simply, that have no connections that loop. The BP ANN receives input by neurons in

the input layer, and the output of the network is given by the neurons on an output

layers. There maybe one or more intermediate hidden layers.

30

In this analysis, the number of layer that been used is 3 layer which consist of

input, hidden layer and output as shown in figure 3.7 below:

Figure 3.7 Three layer network

One hidden layer structure is used in the study conducted by Hornik (1993) and it is

shown that an ANN model with a single hidden layer can approximate any

continuous function to any desired accuracy. The activation function of the artificial

neurons in ANNs implementing the back propagation algorithm is a weighted sum

where the sum of the inputs x multiplied by their respective weightsw ji as shown in

equation 1 below:

(3.1)

Base on the equation the activation depends only on the inputs and the

weights. If the output function would be the identity (output = activation), then the

neuron would be called linear. But these have severe limitations. The most common

output function is the sigmoidal function as shown in equation 2 below:

31

(3.2)

The sigmoidal function is very close to one for large positive numbers, 0.5 at zero,

and very close to zero for large negative numbers. This allows a smooth transition

between the low and high output of the neuron (close to zero or close to one). We

can see that the output depends only in the activation, which in turn depends on the

values of the inputs and their respective weights. Now, the goal of the training

process is to obtain a desired output when certain inputs are given. Since the error is

the difference between the actual and the desired output, the error depends on the

weights, and we need to adjust the weights in order to minimize the error. We can

define the error function for the output of each neuron by the equation 3 below:

(3.3)

The difference between the output and the desired target because it will be

always positive, and because it will be greater if the difference is big, and lesser if the

difference is small. The error of the network will simply be the sum of the errors of all

the neurons in the output layer:

(3.4)

The back propagation algorithm now calculates how the error depends on the output,

inputs, and weights. The weight can be adjust using the method of gradient

descendent:

(3.5)

This formula can be interpreted in the following way: the adjustment of each weight

(Δ W ji ) will be the negative of a constant eta (η) multiplied by the dependence of the

previous weight on the error of the network, which is the derivative of E in respect to

w . The size of the adjustment will depend on 0, and on the contribution of the weight

to the error of the function.

When the results from the BP ANN had been obtained, the results will be

compared with the normal regression method and also possibly ARIMA. The reason

for this comparisons is to analyze the error analysis in order to prove that BP ANN

32

produced less error than the other two method. Other than that, when all the data

had been analyze it is will also be divided into floor area in order to determine the

electricity consumption on each different floors. It is also to study on the equipment

that contribute to a larger energy consumption in the office building. Plus by using

the data that had been analyze the student will be able to study the trend or pattern

system of each of the increasing and decreasing of the electricity consumption

according to the data baseline. By study the events and reason for the pattern then

an energy efficiency can be implemented base on the practical strategy. Then the

student will be able to take an action in order to optimize and the energy

consumption based on the floor area and the trend of the system. In this case, the

implementation method that had been selected is the solar system at the rooftop of

the office building. Currently the rooftop of the office building is already been install

with the solar system, but it is not fully covered the whole floor of the rooftop side. As

the contribution to the data donator, the student will calculate the amount of solar

panel that will be use in the rooftop if it is fully equipped. The student will also

calculate the amount and percentage of the energy that can be save through the fully

implementation of the solar system at the full scale locate at the office building

rooftop.

B.) Radial Basic Function

An RBF is a two layer feed forward type of network which the input was

transformed by the basis function that located at the hidden layer. Different from the

perceptron type network which the activation of hidden units is based on the dot

product between the input vector and a weight vector, the activation of the RBF

hidden units is based on the distance between the input vector and a prototype

vector. At the output layer, linear combinations of the hidden layer node responses

are added to form the output. The name RBF comes from the fact that the basic

functions in the hidden layer nodes are radially symmetric. The common choice of

the basic function that been used is the Gaussian function which can be defined by a

mean and standard deviation. The RBF consists a number of interesting properties

where there is exist strong connections to a number of scientific disciplines and

33

these include the function approximation, regulation theory, density estimation and

interpolation in the presence of noise. RBF also allow for a straight forward

interpretation of the internal representation that had been produced by the hidden

layer which cause the training algorithms for RBF are significantly faster than those

Multilayer Perceptron (MLP).

RBF have the origin in theirs technique for performing exact function

interpolation. These technique place a basis function at each training example as

shown below:

h ( x )=∑k=1NW k ∂ (||X−XN||)=∅W (3.6)

Compute the coefficients so that the “mixture model” has zero error at those

examples

(3.7)

Figure 3.8 RBF Function Interpolation

The RBF are the feed forward networks consist of a hidden layer of a radial

kernels and an output layer of linear neurons. The two layers carry entirely different

role as the hidden layer performs a non-linear transformation of input space which

resulting hidden space is typically of higher dimensionality than the input space.

Meanwhile the output layer performs linear regression to predict the desired targets.

The reason for using non-linear transformation followed by a linear one is based on 34

the Cover’s theorem on the separability of patterns which is a complex pattern

classification problem cast in a high dimensional space non-linear is more likely to be

linearly separable than in a low dimensional.

Output units form linear combinations of the hidden unit activations to predict

the output variables. The activation of an output unit is determined by the DOT-

PRODUCT between the hidden activation vectors and the weight vector w is

shown below:

(3.8)

For convenience, an additional basis function with a constant activation of 1 can

be used to absorb the bias term .

Figure 3.9 Schematic Diagram of RBF Network

35

Table 3.10 Advantage and Disadvantage of MLP versus RBF

Advantages Disadvantage

MLP Good mathematical

foundation

If solution exist it can

be found

MLP work easily with

continuous values

Deals well with noise

MLP does not scale well

Once trained MLP is not

updated without retraining

Retraining does not

preserve pervious MLP

knowledge

MLP depends entirely on

the algorithms used to

create it

RBF Train faster than MLP

The hidden layer is

easier to interpret than

the hidden layer in an

MLP

It is slower to used

compared to MLP

If the speed is the factor

MLP is more appropriate

3.8.2 Network Training

The BP ANN using the supervised learning which a learning method that has

teacher signal or targets. In supervised learning the training patterns are provided to

the ANN together with a teaching signal or target. The difference between the ANN

output and the target is the error signal. Initially the output of the ANN gives a large

error during the learning phase. The error is then minimized through continuous

adaption of the weights to solve the problem through a learning algorithm. In the end,

when the error becomes very small, the ANN is assumed to have learned the task

and training is stopped. It then can be used to solve the task in the recall phase. The

idea of BP algorithm is to reduce this error, until the ANN learns the training data. 36

The training begins with random weights, and the goal is to adjust them so

that the error will be minimal. The configuration of the learning are shown in figure

below:

Figure 3.10 Supervised learning configuration

3.9 Summary

As the summary, in order to train the BP ANN we first must have a data were

we will be able to create a baseline in order to study on the trend of the system.

Other than that, a prediction by using the BP ANN will be used as a part of the

targeting process in order to predict the energy consumption that can be save by

applying the energy efficiency.

37

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Introduction

Energy consumption in a building is effected by several causes and factors

that will determine the increasing or reducing of the energy based on the monthly bill

of electricity. The factors that affect the energy consumption is considered differently

based on the type of the building itself. Some of the buildings may share the same

factors, while others may not base on the correlation factor between the relationships

of the variables itself. The energy consumption cause and effect can be either in

term of weather, number of machinery, number of people in the building, operating

hours, floor area, the behavior of the end user and others. All of these factors are

among the most factors that taking for consideration when conducting an energy

audit and energy efficiency. Energy efficiency is the method or process that involving

the campaign, the change of machinery, lighting, the building management system

configuration and others in order to obtain the most efficient and effective energy

consumption which will lead to reduce of the kilowatt hours and at the same time

reduce the monthly bill without effecting the comfort of the end users. Before

implementation of the energy efficiency, an energy audit need to be conducted first in

order to investigate the potential of energy that can be saved also to determine the

potential of area in the building that can be save in terms of machinery, lighting,

system configuration based on the data of the collection on a specific building. For

38

this research the type of building that been investigate for the energy audit is

commercial building 4G8 and 4G10. This research also focus on the prediction

method by using artificial neural network in order to investigate the most accurate

method for prediction in order to provide the baseline for the industry.

4.2 Energy Generation and Cost Saving

4.2.1 Prediction Energy Consumption By Using Artificial Neural Network

The purpose of this research has been mentioned in the objective

earlier which is to prediction the next energy consumption in the

building by using a new method which is prediction based on artificial

neural network. In the ANN, it contain two different method for the

prediction purpose which is Multilayer Perceptron (MLP) and Radical

Basic Function (RBF). At the earlier stage the both method were test

using a same set of data in order to selected the most accurate and the

less error in the prediction in order to compare with the traditional

method that already been used in to develop the baseline of energy

consumption which is Linear Regression method.

1.) Comparison Between Radial Basic Function and Multilayer Perceptron

The Artificial Neural Network is a non-linear process which apply the

principle of learning to behave. An Artificial Neural Network need to be

train well before applied for application. In this research the type of

Artificial Neural Network that had been used is Multilayer Perceptron and

Radial Basic Function. This two method of ANN have a different

characteristic and behavior.

39

For the traditional ANN which is Multilayer Perceptron architecture is very

inefficient for problem solving compare to other ANN architectures plus

MLP also require more challenging computation. Meanwhile for the Radial

Basic Function it is more simple which only consist of fixed three layer only

so the training process is generally faster than the MLP. It also act as local

approximation networks, because the network output are determined by

specific hidden units in certain local receptive field compare to MLP which

is working globally, since the network outputs are decides by all the

neurons. In this research, the main purpose of this two method is to

compare which method is more accurate based on the actual value and

also the rated of error for each method. The prediction of the two method

were compared on the graph below:

40

0.00

100,000.00

200,000.00

300,000.00

400,000.00

500,000.00

600,000.00

Skywarth Building MLP vs RBF

ELECTRICITY CONSUMPTIONMLPRBF

MONTH

ELECTRICITY CONSUMPTION

Graph 4.1 Data Comparison for Skywarth Building Using RBF and MLP

Graph 4.2 Data Comparison for Skymage Building Using RBF and MLP

The accuracy of the method cannot be determined by only observation

based on the graph but the most important is to determine the error of

each method through Root Mean Square Error formula:

RMSE=√∑i=1

n

( yi− yi)2

n (4.1)

41

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

0

100000

200000

300000

400000

500000

600000

700000

800000

900000Skymage Building MLP vs RBF

ELECTRICITY CONSUMPTIONMLPRBF

MONTH

ELECTRICITY CONSUMPTION

Table 4.1 Error Data Tabulation

Method Error Sqt

Skywarth Building (4G8)Multilayer Perceptron 24626.79

Radial Basic Function 25480.46

Skymage Building (4G10)Multilayer Perceptron 57532.99

Radial Basic Function 66970.32

Based on table Table 4.1, the most accurate method for the prediction is

the Multilayer Perceptron as it has the lowest error rate compare to the

Radial Basic Function. One of the reason for this matter is the data set is

large enough for the MLP to train and test.

2.) Comparison of Artificial Neural Network and Linear Regression

In this research, the comparison between the methods involved are the two

types of Artificial Neural Network which is Multi-Layer Perceptron and Radial

Basic Function. The two Artificial Neural Network method were compare with

the traditional method that had been used in the industry for energy efficiency

process in order to predict the next energy consumption and also to develop a

baseline for a certain or specific building. This comparison is not based on

linear or non-linear system but more on the method that is prefered by the

industry. The Graph 4.3 shows the comparison of all the three methods which

is MLP, RBF and Linear regression for both buildings:

42

Graph 4.3 Data Comparison for Skywarth Building Using RBF, MLP and

Linear Regression

Graph 4.4 Data Comparison for Skymage Building Using RBF, MLP and

Linear Regression

43

0.00

100,000.00

200,000.00

300,000.00

400,000.00

500,000.00

600,000.00Building Skywarth MLP vs RBF

ELECTRICITY CONSUMPTIONLinear RegressionMLPRBF

MONTH

ELECTRICITY CONSUMPTION

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

0

100000

200000

300000

400000

500000

600000

700000

800000

900000 Skymage Building MLP vs RBF

ELECTRICITY CONSUMPTIONLiniear RegressionMLPRBF

MONTH

ELECTRICITY CONSUMPTION

The accuracy of the three methods were measured based on the error square

the method as shown in Table 4.9 below:

Table 4.2 Error Data Tabulation

Method Error Sqt

Skywarth Building (4G8)Multilayer Perceptron 24626.79

Radial Basic Function 25480.46

Linear Regression 30491.23

Skymage Building (4G10)Multilayer Perceptron 57532.99

Radial Basic Function 66970.32

Linear Regression 91738.31

Based on the Table 4.2, the most accurate method that can be used for

prediction is Multilayer Perceptron as it has the lowest error rate for both

Skywarth and Skymage building. Meanwhile Linear Regression consist a lot

error plus the prediction by using Linear Regression is mostly the values is in

the average of the real value (kWh).

44

3.) Energy Cost Saving

Based on the prediction by the three method, this research provide the

baseline for the Sywarth and Skymage building. The purpose of the

baseline is to develop the Building Energy Index (BEI) for each of the

building. Energy Energy Index is a representation for the annual energy

consumption per meter square of a facility. The BEI is used to compare

energy consumption relative to similar building types or to track down the

energy consumption from year to year in the same building. The BEI can

be reduce by applying the energy efficiency approach that had been

selected in this research which is retrofitting method. Retrofitting is an

approach that applied to the building lighting system, where the normal

fluorescent tube with 36W, compact fluorescent downlight with 18W and

26W and 50W halogen is changed to equivalent LED product. The

baseline was selected starting from January 2013 until December 2013.

The amount of saving based on the most accurate prediction which is

Multilayer Perceptron compare with the other two methods are shown in

Table 4.3 below:

Table 4.3 Total Cost Saving based on Baseline January 2013 Until

December 2013

Method Total Cost Saving

Skywarth BuildingMultilayer Perceptron RM55,639.15

Radial Basic Function RM34,741.74

Linear Regression RM82,553.30

Skymage BuildingMultilayer Perceptron RM147,387.76

Radial Basic Function RM147,639.60

Linear Regression RM113,876.32

45

Based on Table 4.3, the most saving method for the Skwywarth building is the

Linear Regression as the amount of cost that can be save is worth

RM82,553.30 from January 2013 until December 2013. Even though the cost

saving by using Linear Regression is the highest among the three method but

the consideration will based on the error square of the method. In this case,

the most accurate method with the lowest error square is the Multilayer

Perceptron and the cost saving by using Multilayer Perceptron is RM55,

639.15 for the first building, the cost that can be saved is relevant among the

three method. The reason for this matter is the error for three method in based

on the Skywarth building is close to each other and the trend for actual energy

consumption is not that fluctuate which mean the energy in the Skywarth

building is been monitor in term of the usage in the building. But a different

situation happen in the Skymage building as the actual energy consumption is

highly fluctuate which mean the energy consumption for this building is totally

not been monitored and supervised. For the Skymage building the highest

cost saving is by using the Radial Basic Function method which worth RM147,

639.60. Even though the amount of the saving is very encouraging but the

error for the Radial Basic Function in Skymage building, is 66970.32327.

Based on the error, the amount of saving using the Linear Regression method

cannot be considered. The method with the less error for the Skymage

building is the Multilayer Perceptron with the error value of 57532.9886 and

the total amount of saving worth RM147, 387.76

In this research, the energy efficiency approach that been consider is the

retrofitting for the lighting system. Both building is taking the same approach in

order to improve the energy consumption and the total cost to implement this

method is worth RM499, 539.00 for each. The value of investment for the both

building is consider the same as both building is not fully occupied based on

the building net usable area. Table 4.4 and Table 4.5 below shown the

Building Energy Index based on the baseline, the Building Energy Index

based on the baseline prediction by using Multilayer Perceptron, potential

46

energy saving based on the energy efficiency approach and the return of

investment:

Table 4.4 Building Energy Index for Skywarth Building

Skywarth Building BEI BaselineAnnual Electricity Consumption (Actual) (kWh) 5,907,068.00

Building GFA (exclude basement) (m²) 50,064

Building Nett Usable areas (m²) 38,187

BEI based on GFA (kWh/m²/year) 118.00

BEI based on Nett Usable/Lettable Areas (kWh/m²/year) 154.69

Annual Electricity Consumption (MLP)(kWh) 5,754,631.98

BEI based on GFA (kWh/m²/year) 114.95

BEI based on Nett Usable/Lettable Areas (kWh/m²/year) 150.70

Potential Saving (RM) 55,639.15

Energy Efficiency Retrofitting Approach Investment (RM) 499,539.00

Return of Investment, ROI (Years) 8.97

Table 4.5 Building Energy Index for Skymage Building

Symage Building BEI BaselineAnnual Electricity Consumption (Actual) (kWh) 9,219,390.00

Building GFA (exclude basement) (m²) 71,597.80

Building Nett Usable areas (m²) 54,005

BEI based on GFA (kWh/m²/year) 128.77

BEI based on Nett Usable/Lettable Areas (kWh/m²/year) 170.71

Annual Electricity Consumption (MLP)(kWh) 8,815,587.90

BEI based on GFA (kWh/m²/year) 123.13

BEI based on Nett Usable/Lettable Areas (kWh/m²/year) 163.24

Potential Saving (RM) 147,387.77

Energy Efficency Approach Investment (RM) 499,539.00

47

Return of Investment (Years) 3.39

4.3 Summary

Based on all the results, the most accurate method that can be considered

for the prediction of energy consumption for energy efficiency in industry is the

Artificial Neural Network. The results had proved that the ANN method is more

accurate compared to the traditional method that had been used in the industry for

the energy efficiency sector. This chapter also come out with a relevant cost saving

based on the method that obtain the less error.

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CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

As a conclusion, this research has analyzed and mitigated the energy efficiency

using artificial neural network as the most accurate method compared to the traditional

method which is Linear Regression. This research also has determined the energy

accounting in order to identify the highest contribution equipment or system in the

commercial building Skywarth and Skymage in term of energy consumption. This

research has identified nine of the potential energy efficiency approach for the Skywarth

and Skymage commercial building. This research has successfully achieved the target

objective of the thesis. Finally, from the analysis that had been conducted there are

some guidelines that can be purpose to the industry in order to improve the energy

consumption and cost saving. The guidelines are too conduct the energy audit in order

to identify the factors that will influence the energy consumption in the building. The next

step is to identify the potential of the energy efficiency approach that can be conduct in

a certain building. Based on the previous bill, develop a baseline in order to determine

the amount of saving that can be save through the potential energy efficiency approach

that had been consider based on the major system that will be reduce. Then come out

with the cost for the implementation of the energy efficiency approach that had been

selected based on the amount that each building owner intend to investment starting

49

from the low cost part. Based on this guideline, the energy efficiency will be conducted

with a more proper methods compared to the existing approach by the industry

5.2 Recommendations

There are several recommendations for future work in order to improve the

current research. First is to add extra method such as ARIMA in order to determine

more accurate method to develop the baseline for the energy efficiency in the

commercial building. Second is the element of design in terms of new system that can

be applied to the current commercial building in order to save the electricity. This

system can be either mechanical or electrical part such as the design pump, chilled or

lighting arrangement in the building. Furthermore, a future recommendation that can be

added is a research which focus on the method towards energy efficiency in the building

by considering several extra factors instead of one factor such as the number of

equipment, the building façade and operating schedule in the building to see the

relationship between each factors and analyze the correlation between the variables.

Lastly, the implantation of solar system to the building by designing the system itself.

50

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Appendix 1

Radial Basic Function Network Structure

54

Case Processing SummaryN Percent

Sample

Training

23 85.2%

Testing 4 14.8%Valid 27 100.0%Excluded 6Total 33

Network Information

Input LayerFactors 1 TEMPNumber of Units 15

Hidden Layer

Number of Units 4a

Activation Function Softmax

Output Layer

Dependent Variables

1 KKLW_4G8

Number of Units 1Rescaling Method for Scale Dependents

Standardized

Activation Function Identity

Error FunctionSum of Squares

a. Determined by the testing data criterion: The "best" number of hidden units is the one that yields the smallest error in the testing data.

55

Appendix 2

Multilayer Perceptron Network Structure

56

Case Processing SummaryN Percent

SampleTraining 22 84.6%Testing 4 15.4%

Valid 26 100.0%Excluded 7Total 33

Network Information

Input LayerFactors 1 TEMPNumber of Unitsa 14

Hidden Layer(s)Number of Hidden Layers 1Number of Units in Hidden Layer 1a 1Activation Function Sigmoid

Output Layer

Dependent Variables 1 KKLW_4G8Number of Units 1Rescaling Method for Scale Dependents NormalizedActivation Function SigmoidError Function Sum of Squares

a. Excluding the bias unit

57