CITATION: Chiroma, Haruna, et al. "An Intelligent Modeling of Oil

Consumption." Advances in Intelligent Informatics. Springer International Publishing, 2015. 557-568.

An Intelligent Modeling of Oil Consumption

Haruna Chiroma1,2, Sameem Abdulkareem

1, Sanah Abdullahi Muaz

3, Adamu I.

Abubakar4, Edi Sutoyo

5, M. Mungad

5, Younes Saadi

5, Eka Novita Sari

6, and Tutut

Herawan5

1Department of Artificial Intelligence 3Department of Software Engineering 5Department of Information systems

University of Malaya

50603 Pantai Valley, Kuala Lumpur, Malaysia 2Federal College of Education (Technical)

School of Science

Deapartment of Computer Science, Gombe, Nigeria 4Department of Information system

International Islamic University

Gombak, Kuala Lumpur, Malaysia 6AMCS Research Center, Yogyakarta, Indonesia

hchiroma@acm.org, samaaz.csc@buk.edu.ng, 100adamu@gmail.com,

younessaadi@gmail.com, eka@amcs.co,

{sameem,edisutoyo,mungad,tutut}@um.edu.my

Abstract. In this study, we select Middle East countries involving Jordan,

Lebanon, Oman, and Saudi Arabia for modeling oil consumption based on

computational intelligence methods. The limitations associated with Levenberg-

Marquardt (LM) Neural Network (NN) motivated this research to optimize the

parameters of NN through Artificial Bee Colony Algorithm (ABC-LM) to build

a model for the prediction of oil consumption. The proposed model was

competent to predict oil consumption with improved accuracy and convergence

speed. The ABC-LM performs better than the standard LMNN, Genetically

optimized NN, and Back-propagation NN. The proposed model may guide

policy makers in the formulation of domestic and international policies related

to oil consumption and economic development. The approach presented in the

study can easily be implemented into a software for use by the government of

Jordan, Lebanon, Oman, and Saudi Arabia.

Keywords: Artificial Bee Colony; Neural Network; Levenberg-Marquardt; Oil

Consumption; Prediction.

1 Introduction

A significant amount of energy supply to the world market comes from the Middle

East. Yet, there are countries in the Middle East that significantly depend on oil

importation for domestic consumption. Thus, we propose to model oil consumption in

some selected Middle East countries. Jordan and Lebanon were selected due to their

insufficient production of oil. Saudi Arabia was involved in the case study of its role

in supplying significant quantity of oil to the global market and being a member of the

Organization of the Petroleum Exporting Countries (OPEC). Oman attracted attention

for involvement in the study as a result of its significant production of crude oil and

the country is not a member of the OPEC [1].

There are studies on modeling of energy demand and consumption in the literature,

for example, Assure et al. [2] applied Particle Swarm Optimization (PSO) and

Genetic Algorithm (GA) to estimate oil demand in Iran based on socioeconomic

indicators. The key indicators of energy demand in Turkey such as population, Gross

Domestic Product (GDP), import and export were used for energy demand estimation

in Turkey using Ant Colony Optimization (ACO) [3]. Chiroma et al. [4] applied Co-

Active Neuro Fuzzy Inference System (CANFIS) because of its strengths over Fuzzy

Neural Network (NN) and Adaptive Network-based Fuzzy Inference System

(ANFIS). The CANFIS was modelled to predict crude oil price and comparative

analysis shows that the CANFIS was found to perform better than the ANFIS. To

improve the effectiveness of the CANFIS, Chiroma et al. [5] model the consumption

of energy in Greece based on the hybridization of GA and CANFIS. It was found that

the propose approach performs better than the comparison methods including

CANFIS. Kaynar et al. [6] proposes NN and neuro–fuzzy system to predict natural

gas consumption in Turkey. Results indicated that the prediction accuracy of the

ANFIS was better than the Autoregressive Integrated Moving Average (ARIMA),

multi-layer perceptron, and radial basis function networks. Malaysia oil production

was estimated using ARIMA by [7]. However, the ARIMA typically assume normal

distribution for input data which makes the ARIMA unsuitable for modeling energy

because of the high level of energy uncertainty [8].

Applications of GA in training NN require lengthy local searches close to a local

optimum. In GA, if population changes, the GA abolished the preceding knowledge

of the problem [9]. The PSO has the possibility of being stuck in local optimal which

can undermine its effectiveness [9]. Despite the effectiveness of NN in solving linear,

nonlinear, and complex problems, the NN can possibly be trapped in local minima

and over-fit the training data. There is no ideal framework for determining the optimal

structure of the NN and the selection of the initial training parameters. Researchers

typically employ the cumbersome trial-and-error technique to determine the optimal

structure and selection of the NN parameters [10]. However, Karaboga and Basturk

[11] reported that several comparative studies proved that the performance of the

Artificial Bee Colony algorithm (ABC) outperforms GA, ACO, PSO, and Differential

Evolution algorithms.

In this study, we propose to hybridize ABC and Levenberg-Marquardt NN (LM)

due to its accuracy and convergence speed over other fast learning algorithms [12]

and computational efficiency [13] in order to improve prediction accuracy and

convergence speed in modeling oil consumption. The hybridization of ABC and LM

(ABC-LM) is not a new concept, but applying the concept in the modeling of oil

consumption in Jordan, Lebanon, Oman, and Saudi Arabia is the innovation of this

study.

The rest of this paper is organized as follows. Section 2 describes the proposed

method and data collection. Section 3 describes experimental results and discussion.

Finally the conclusion and future works are presented in Section 4.

2 Proposed Method

2.1 Artificial Bee Colony

In the ABC algorithm, the colony of artificial bees (AB) involve three (3) major

stages, including: employed bees, onlookers, and the scout. The first portion of the

colony comprises of the employed AB, whereas the second portion contain onlookers.

For each of the food source, only one employed bee exists. The employed bee of the

discarded food source becomes the scout. The execution of ABC can be summarized

in the following steps [11]:

Initialize population with random possible solutions

REPEAT

Step 1: Move the employed bees onto their food sources and determine their nectar

amounts.

Step 2: Move the onlookers onto the food sources and determine their nectar

amounts.

Step 3: Move the scouts for searching new food sources.

Step 4: Memorize the best food source found so far and continue

UNTIL (requirements are met and optimal solution found).

2.3 Data Collection

The ABC-LM is a data driven model, therefore, require data and careful design

for successful modeling of the oil consumption. The oil referred to in this paper as

reported by [1] comprised of liquefied petroleum gases, residual fuel, distillate oil,

kerosene, jet fuel, motor gasoline, and other petroleum products. Our goal is to collect

data for domestic oil consumption in Jordan, Lebanon, Oman, and Saudi Arabia. The

oil consumption data were collected from the official website of the Energy

Information Administration of the US department of Energy on a yearly basis for a

period starting from 1981 to 2012. The data were found to be complete without

missing points, outliers, and incomplete information. The oil consumption data are

given in thousands of barrels. The independent variables responsible for domestic oil

consumption are the installed power capacity and the residence yearly/electricity

consumption [14]. The yearly ambient temperature, GDP, the amount of CO2

pollution, number of air conditioners, electricity price, installation of renewable

energy technologies and etc. [14]. According to Khazem [15], data availability is one

of the criteria’s for the selection of variables to be used in modeling. The data set for

modeling in this study were not normalized to prevent the destruction of original

patterns in the historical data [16]. The data were subjected to descriptive analysis in

order to unveil their characteristics. The descriptive statistics of the data sets including

maximum and minimum oil consumption are presented in Table 1. The standard

deviation indicates that the dispersion of the oil consumption do not much deviated

from each other except for Saudi Arabia.

Table 1. Descriptive statistics of the oil consumption

Min. Max. Mean Std. Dev.

Jordan 37.00 112.43 81.63 22.39

Lebanon 33.00 128.86 75.66 29.12

Oman 16.66 144.89 53.01 32.22

Saudi Arabia 610.0 2861.00 1418.79 547.30

2.4 Experiment Setup for Modeling ABC-LM for Oil Consumption

The complete modeling setup is represented in the flowchart shown in Fig. 1. The

major steps involved in the modeling comprised of data collection and preprocessing,

ABC and NN initialization, and evaluation of the proposed method.

Figure 1. The proposed method for intelligent modeling of the oil consumption

The ABC-LM requires data for training and testing the efficiency of the model.

Data partition in modeling has no specific universal acceptable partition ratio. Several

data partition ratios were experimented and 80% for training and 20% for testing the

effectiveness of the ABC-LM model was adopted. The objective of the ABC is to

optimize the NN weights, bias, and hidden neurons. The NN require initialization for

the ABC optimization to start execution. The input neurons were fixed to five (5)

since independent variables in the historical are five (5), and the dependent variable is

the oil consumption because it depends on the independent variables. The hidden

layer of the NN was set to one (1), though, the number of hidden layers can be more

than one (1) but theoretical work of [17] argued that one hidden layer is sufficient for

modeling any function. The activation functions of the hidden and output layer are

sigmoid and linear, respectively, as recommended by [12]. The epoch for the training

was set to 3000. The objective function used for the ABC in the experiment is Mean

Square Error (MSE) because is more preferable than other statistical measures,

especially when comparing different algorithms on the same dataset [18].

Optimization using the ABC involves the searching of optimal values of NN weights,

bias, and a number of hidden layer neurons. For every Bee, it represents an NN for

optimizing the MSE. There are parameters associated with the ABC that requires

settings for efficiency in the search for the best solution. The performance of ABC

depends on the best settings of the parameters which includes: number of sites chosen

out of number of scout bees visited sites, the initial size of the patches, a number of

bees recruited for the selection, number of bees recruited to the best sites, a number

of the elite sites out of selected sites, and number of the scout bees. However, there is

no idle framework for automatically setting the best values that can yield the best

results. Typically, researchers resort to trial-and-error methods for determining the

approximate values of these parameters. In our work, in order to obtain the best

parameter values, we perform thirty seven (37) experimental trials with a small

sample of the dataset in each of the selected countries under study for a fair

determination of the optimal values of these parameters. The parameters that

produced the minimum in all the four countries under study were adopted for the full

scale experiments. The modeling of the ABC-LM continues for 3000 epochs as

mentioned earlier until learning curve with the minimal MSE is returned with the

corresponding weights, hidden layer neurons, and bias. The convergence speed and

the accuracy were recorded. For comparison, the Back-propagation NN (BPNN),

genetically optimized NN (GANN), and standard Levenberg-Marquardt NN (LMNN)

were used to build a model for the prediction of the oil consumption.

3 Results and Discussion

3.1 Comparative Analysis of Oil Consumptions

The proposed ABC-LM was implemented on a machine with the following

configurations: MATLAB 2012b and SPSS Version 20 on a personal computer (HP

L1750, 4 GB RAM, 232.4 GB HDD, 32-bit OS, Intel Core 2 Duo CPU @ 3.00 GHz).

The oil consumption for the countries selected for this study was analyzed using the

statistical t-test for testing the significant difference between oil consumption of the

comparison countries. The results of the analysis are presented in Tables 2-3. The oil

consumption in Jordan was compared to that of Lebanon for fair comparison because

both countries don’t produce significant amount of oil and heavily depends on oil

import for domestic use. The t-test analysis was performed under the assumption that

the oil consumption in Jordan and Lebanon is equal. The t-test results for the two

countries are shown in Table 2.

Table 2. T-test results (p-value = 0.05, 95% interval) for oil consumption in Jordan and

Lebanon

t df Sig.

Mean

Difference

Jordan 20.628 31 0.000 81.63045

Lebanon 14.698 31 0.000 75.66600

The t-test results indicated in Table 2, shows that there is a significant difference

between the oil consumption in Jordan and Lebanon. Therefore, we concludes that the

oil consumption in Jordan is significantly higher than the oil consumption in Lebanon.

Meaning that the GDP of Jordan is better than that of Lebanon since oil consumption

is positively related to GDP [14]. Hence, the industrial and economic activities in

Jordan could probably be higher compared to Lebanon as economic development is

highly driven by oil consumption.

Table 3. T-test results (p-value = 0.05, 95% interval) for oil consumption in Oman and Saudi

Arabia

t df Sig.

Mean

Difference

Oman 9.873 23 0.000 62.97208

Saudi Arabia 16.578 23 0.000 1619.72714

Revenue generation in both Oman and Saudi Arabia heavily depend on the sales

of oil and they both produce significant amount of oil to the global oil market. The t-

test results presented in Table 3 was performed under the assumption that the oil

consumption in both Oman and Saudi Arabia is equal. Table 3 shows that there is a

significant difference between oil consumption in Oman and Saudi Arabia.

Therefore, the oil consumption in Saudi Arabia is significantly higher than that of

Oman. The probable reason could be attributed to economic development in Saudi

Arabia because oil consumption influences growth in GDP. The mean oil

consumption for each of the countries as shown in Tables 2-3 indicated that Saudi

Arabia has a significantly higher level of oil consumption than Jordan, Lebanon,

Oman, and Saudi Arabia. Meaning that among the countries considered as our case

study, the GDP of Saudi Arabia outperforms that of the other countries. This implies

boom in economic development and industrial production. Thus, estimation of oil

consumption become imperative for development planning in these countries.

3.2 ABC-LM for the Oil Consumption

The optimal parameters used for the modeling of oil consumption based on ABC-

LM as described in section 2.4 are presented in Table 4. The parameters in Table 4

were realized after the best results were found at the end of the experimental trials in

modeling the oil consumption.

Table 4. Parameters of the ABC-LM model

The optimal parameter Setting

Number of sites chosen out of number of scout bees visited sites 32

Number of elite sites out of m selected sites 2

Number of scout bees 150

Number of bees recruited for the best e sites 50

Number of bees recruited for the selection 20

The initial size of the patches 0.5

Number of NN input neurons 5

Number of NN hidden neurons 7

NN hidden layer Activation function Sigmoid

NN output layer activation function Liner

NN Number of hidden layers 1

Number of output neurons 1

Epoch 3000

The best error convergence curve of the ABC-LM model is depicted in Fig. 2. The

curve has no oscillation which signifies the smooth convergence to the best fitness

function (MSE). The straight line at the end of the curve indicates convergence at a

point where the optimum fitness could not improve. This phenomenon indicates

successful convergence [19].

Figure 2. Convergence of ABC-LM on the training dataset.

The ABC-LM model was used to predict oil consumption in the four countries

under study. The oil consumption data from the 2007 to 2012 was reserved for testing

the effectiveness of our proposed ABC-LM model. Figure 3 shows the prediction of

oil consumption for each of the countries. The group bar charts indicate the actual and

predicted oil consumption for each country. The actual oil consumption for Jordan is

represented as Jordan, whereas the predicted oil consumption is represented as predict

JOC. The actual oil consumption in Lebanon is represented as Lebanon, whereas the

predicted oil consumption is represented as predict LOC. The actual oil consumption

in Oman is represented as Oman whereas the predicted oil consumption is represented

as predict OOC. The actual oil consumption in Saudi Arabia is represented as Saudi

Arabia whereas the predicted oil consumption is represented as predict SOC.

Figure 3. ABC-LM model predicted oil consumption and actual oil consumption.

The performance of the ABC-LM model was compared with that of other soft

computing techniques. The standard LMNN, GANN, and BPNN were used to predict

oil consumption in all the countries under study. The best results were found in the

Saudi Arabia oil market and reported in Table 5 for the purpose of comparing with

our approach. The ABC-LM as shown in Table 5 is better than the comparison

algorithms in terms of both accuracy and convergence speed which are the key

indicators of measuring performance in soft computing, machine learning, and data

mining. The performance of the ABC-LM could probably be attributed to the realistic

search of the ABC to locate an exact solution and avoid the limitations of the LMNN.

Table 5. Comparing performance of ABC-LM, LMNN, GANN, and BPNN

Algorithm CPU time Accuracy (MSE)

LMNN 347 0.5185

GANN 238 0.1618

BPNN 593 0.7211

ABC-LM 192 0.0014

Accurate prediction of oil consumption can improve effective allocation of

resources, distribution of oil in a country, tackling of an oil shortage. Also, it can

guide in accurate formulation of international policy related to oil consumption. The

ABC-LM model built for the prediction can be used by Jordan, Lebanon, Oman, and

Saudi Arabia for the prediction of future oil consumption. The model proposes in this

research can help in the formulation of oil consumption policy related to economic

development in the context of Jordan, Lebanon, Oman, and Saudi Arabia. General,

planning of industrial activities as well as development of an effective risk analysis

framework for the countries can be achieved while considering ABC-LM model as an

advisory mechanism. Our proposal does not mean the replacement of experts and

decision makers in the process of taking decisions but to complement their efforts so

that better decisions can be achieved as compared to the lack of decision support

tools.

4 Conclusion and Future Work

In this study, an ABC-LM model is proposed to predict oil consumption in four

Middle East countries comprising of Jordan, Lebanon, Oman and Saudi Arabia. The

performance of the ABC-LM was compared with that of BPNN, LMNN, and GANN

and it was found that the ABC-LM outperforms the comparison algorithms. The

ABC-LM model developed in this research can constitute an alternative method for

the prediction of oil consumption as well as a guide for the formulation of

international and domestic policy related to oil consumption. Hence, sustainability

can be enhanced for the development of economic growth, thereby, creates job

opportunities for the unemployed population in the countries under study. The

methodology documented in this study can easily be modified to predict oil

consumption for other Middle East countries such as Iran, Iraq, Kuwait, Yemen, and

etc. as well as modeling of oil consumption in OPEC countries.

In the future we will extend this research to include countries from North America

and Europe. Our next direction is to find out whether oil consumption of OPEC

countries is positively related? If so; can computationally intelligent algorithms be

applied to predict oil consumption in an OPEC country based on oil consumption in

other OPEC countries?.

Acknowledgment. This work is supported by University of Malaya High Impact

Research Grant no vote UM.C/625/HIR/MOHE/SC/13/2 from Ministry of Higher

Education Malaysia.

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