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Selection of the best ARMAX model for forecasting energy demand: case study of the residential and commercial sectors in Iran

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Abstract

The main purpose of the present study is to develop a simple yet proper top-down model for forecasting the energy demand of the residential and commercial sectors in Iran. This model can be used as a tool of scenario analysis to predict the emerging energy demand in future. The proposed model would be systematically developed and selected based on various quantified exogenous variables. For this purpose, a certain model out of a collection of 41,472 parallel models with different inputs and dynamics is chosen as the most appropriate model. According to the logical conjunctive relationships between the variables, the structure of all competing models is established to log-linear. Different possible combinations of various measures for the exogenous variables generate parallel models. Then, an automated fuzzy decision-making (FDM) process determines the best model. Finally, defining several scenarios, the energy demand of the residential and commercial sectors in Iran for the period of 2013 to 2021 is forecasted. The results showed that despite of de-subsidization, which is included by a dummy variable, the energy demand will grow by an average rate of about 3 % annually.

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Notes

  1. Since 1980 till present, the average 10-year share of industrial sector from total value added of the country has been increased by almost 10 % each decade.

  2. Kwiatkowski–Phillips–Schmidt–Shin test

Abbreviations

AAEP:

Average absolute error percentage

BIRR:

Bilion Iranian Rials

BLD:

Book value of active buildings [104 BIRR]

BU:

Bottom-up

D:

Differencing operator

DC:

Dynamicity criterion or the property index for the dynamics of a model

DUM:

Dummy variable

ER:

Energy consumption by the residential and commercial sectors [MTOE]

FIT:

Fitness of simulation

FPE:

Akaike’s final prediction error

GT:

The global Student t statistics

HNT:

Household number [million persons]

HVAC:

Heating, ventilation, and air conditioning

INVC:

Gross fixed capital formation (investment) for the constructions [104 BIRR]

JB:

Jarque-Bera tests of normality

KBLD:

Cumulative sum of BLD [104 BIRR]

KCD:

The same variable (KCT) calculated considering a 5 % annual depreciation rate

KCT:

Total capital invested for buildings (constructions) [104 BIRR]

L:

Logarithm

LB:

Ljung-Box independence statistics

LBR:

Labor [million persons]

MTOE:

Million (metric) tons oil equivalent

OLS:

Ordinary least squares

ONE:

A vector of “ones” with an appropreate dimension

PAPL:

Electrical and fuel appliance price index; PAPL(1997) = 1

PDF:

Possibility distribution function

PEM:

Prediction error method

PFC:

Consumer energy (fuel) price index; PFC(1997) = 1

PFPG:

Energy price index adjusted (deflated) by the general price index; PFPG(1997) = 1

PNL:

Population minus the labor [million persons]

PO:

Population [million persons]

PP:

Prediction power

PS:

Power spectrum

P th :

Norm of covariance matrix of the parameters

R 2 :

Adjusted explanatory power

SP:

Simulation power

TD:

Top-down

T min :

The least Student t statistics among all

U(x):

The utility function

VAN:

The total value added minus that of the oil sector [103 BIRR]

VAT:

Value added of all economic sectors (total value added) [103 BIRR]

YNI:

The national income [103 BIRR]

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Acknowledgments

The authors are thankful of the private company Idam Consulting Group (ICG) and the Institute for Research, Environment and Sustainability (IRES), UBC, for their supports.

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Authors and Affiliations

  1. School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Hamed Shakouri G.

  2. Institute for Resources, Environment, and Sustainability, UBC, Vancouver, British Columbia, Canada

    Hamed Shakouri G.

  3. Department of Industrial Management, Faculty of Management, University of Tehran, Nasr Bridge, Chamran Highway, PO Box: 14155-6311, Tehran, Iran

    Aliyeh Kazemi

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  1. Hamed Shakouri G.
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  2. Aliyeh Kazemi
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Correspondence to Aliyeh Kazemi.

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Shakouri G., H., Kazemi, A. Selection of the best ARMAX model for forecasting energy demand: case study of the residential and commercial sectors in Iran. Energy Efficiency 9, 339–352 (2016). https://doi.org/10.1007/s12053-015-9368-9

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  • DOI: https://doi.org/10.1007/s12053-015-9368-9

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