# Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques

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## Abstract

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## 1. Introduction

## 2. Discussion of Previous Electricity Demand Studies

#### 2.1. Previous Electricity Demand Studies in Resource-Rich Small Open Developing Economies

#### 2.2. Previous Azerbaijan Electricity Demand Studies

## 3. Electricity Demand Function Specification and Data

#### 3.1. Per Capita Electricity Demand Function

#### 3.2. Data

- $E$ is electricity consumption per capita. This is equal to total final electricity consumption in TWh divided by population in millions. The total final electricity consumption data are collected from the International Energy Agency Database in Mtoe and then converted to TWh in order to be consistent with the electricity price [8]. Population data comes from the World Bank Database [4].
- $P$ is the real electricity price. Nominal retail electricity prices in Azerbaijani New Manat (AZN) are administratively set by the government (being the same for the industrial, residential, and commercial sectors) and are collected from the various Statistical Yearbooks of the State Statistical Committee of the Republic of Azerbaijan [9]. The real electricity prices are in 2005 AZN, found by deflating the nominal prices by the Consumer Price Index (CPI), 2005 = 100 collected from [9].
- $GDP$ is real Non-oil GDP per capita. This is calculated by deflating nominal Non-oil GDP in million AZN by the CPI, 2005 = 100 and then dividing by population in millions. Nominal Non-oil GDP is retrieved from [9].
- $GDPT$ is real total GDP per capita. This is calculated in a similar way to GDP—nominal total GDP in million AZN is deflated by the CPI, 2005 = 100 and then divided by population in millions. Nominal total GDP is retrieved from [9].
- $HDD$ and $CDD$ are heating degree-days and cooling degree-days, respectively. These are summary variables of weather conditions that are considered given they might influence electricity demand. For HDD the reference temperature is 18 °C. For CDD the reference temperature is 21 °C. Both HDD and CDD come from the [46] database, where population-weighted degree-days were constructed for just under 150 countries for the period 1948 to 2013. The weather variables from [46] are calculated using a sophisticated degree-days methodology by addressing issues such as, limited geographical availability, temporal and spatial aggregation, the lack of accounting for various climatic factors, and the restrictive use of a singular reference temperature.

## 4. Cointegration Methodologies and Estimated Models

#### 4.1. Methodologies

#### 4.1.1. Unit Root Test

#### 4.1.2. Different Cointegration Methods

#### The Johansen Cointegration Method

#### Small Sample Bias Correction in the Johansen Method

#### ARDLBT Method

#### Small Sample Bias Correction in ARDLBT Approach

#### EG Approaches

- (i)
- For the first step, estimate a regression equation of the non-stationary variables that are integrated in the same order (usually I(1)):$${y}_{t}={c}_{0}+{\mathrm{c}}_{1}{x}_{t}+{\mathsf{\epsilon}}_{t}$$
- (ii)
- The second step of the EG procedure involves estimating the short-run ECM, which is explained in the next section.

#### 4.1.3. Error Correction Model with the General to Specific Modeling Strategy

#### 4.2. Results

^{2}distribution are greater than the critical values at the 1% significance level, meaning that $e$, $p$, and, $gdp$ are statistically significant. In addition, the multivariate test results for stationarity shown in Panel B indicate that none of the variables are stationary and therefore confirm the univariate UR tests results, given in Table 3.

^{2}distribution with a probability of 0.16, thus leading to the conclusion that both $p$ and $gdp$ are weakly exogenous to the cointegration relationship. This all implies that it is acceptable to proceed from the VECM to the single equation ECM analysis (as discussed in [61] inter alia).

#### 4.3. Discussion of the Estimation Results

## 5. Projections of Future Paths of the Electricity Demand

#### 5.1. Methodology

#### 5.2. Forecast Assumptions

#### 5.3. Forecast Scenarios and Discussion

## 6. Summary and Conclusions

- What is the best cointegration technique for modeling per capita electricity demand in Azerbaijan?Given that for all five cointegration methods considered for modeling per capita electricity demand in Azerbaijan passed the appropriate diagnostic tests, choosing the “best” is very difficult. That said the Johansen method is the “best” and the FMOLS method the “worst” according to a number of forecast statistics presented in Table 10—however there is very little difference between them; although the FMOLS model could be regarded as a marginal outlier given the estimated coefficients are a little different to the other four methods considered.
- What are the estimated price and income elasticities for per capita electricity demand in Azerbaijan?The estimated electricity demand across the five methods vary very little. The estimated short- and long-run price elasticities range from −0.3 to −0.4 and −0.8 to −1.0, respectively—suggesting that although the response to a real electricity price change is inelastic, it is relatively high, being close to unity in the long run. Whereas, the estimated short- and long-run income elasticities range from 0.4 to 0.7 and from 0.1 to 0.2—which is also inelastic in both the short and the long run, but somewhat lower (in absolute terms) than the price elasticity in the long run.
- What do the future scenarios suggest for the development of electricity demand in Azerbaijan through to 2025?Using the estimated ECM models for all five cointegration methods, coupled with assumptions about the relevant drivers of electricity demand, the Business as Usual scenario suggests that Azerbaijan electricity demand in 2025 would be something in the order of 19½ to 21 TWh; thus illustrating an average increase of about 0.6% per annum over the period from 2013 to 2025. Of course, different assumptions about the drivers suggest different demand in the future with the highest predicted electricity demand in 2025 being about 45½ TWh according to the “high” scenario or as low as about 9 TWh according to the “low” scenario.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 4.**Projected time paths of the electricity consumption, TWh. (Note: the red, green, and grey lines indicate projected values from the “high”, Business as Usual and “low” scenarios, respectively).

Study | Period | Country or Region | Sector | Methodology | Income Elasticity | Price Elasticity | ||
---|---|---|---|---|---|---|---|---|

Short-Run | Long-Run | Short-Run | Long-Run | |||||

Al-Sahlawi (1990) [22] | 1970–1985 | Saudi Arabia | Aggregate | OLS | 0.37 | 1.02 | Not reported | Not reported |

Eltony and Mohammad (1993) [23] | 1975–1989 | GCC | Residential | OLS | 0.20 | 0.20 | −0.14 | −0.14 |

Commercial | 1.12 | 2.37 | −0.20 | −0.41 | ||||

Industrial | 0.60 | 0.89 | −0.14 | −0.20 | ||||

Eltony (1995) [19] | 1974–1989 | Kuwait | Residential | OLS | 0.09 | 0.57 | −0.06 | −0.39 |

Commercial | 0.11 | 1.93 | −0.13 | −2.20 | ||||

Industrial | 0.02 | 0.13 | −0.05 | −0.27 | ||||

Eltony and Hoque (1997) [24] | 1975–1994 | Kuwait | Aggregate | ECM | 0.57 | 0.65 | −1.09 | −1.97 |

Commercial | 0.43 | 0.62 | −0.27 | −0.35 | ||||

Diabi (1998) [25] | 1980–1992 | Saudi Arabia | Aggregate | OLS, GLS, MLE, CHTA and CCTA | 0.05 to 0.33 | 0.09 to 0.49 | −0.003 to −0.12 | −0.14 to 0.01 |

Al-Sahlawi (1999) [26] | 1975–1996 | Saudi Arabia | Aggregate | OLS | 0.21 | 1.60 | −0.06 | −0.46 |

Residential | 0.13 | 0.70 | −0.10 | −0.50 | ||||

Industrial | 0.08 | 0.66 | No price variable included | |||||

Al-Faris (2002) [20] | 1970–1997 | Saudi Arabia | Aggregate | Johansen cointegration | 0.05 | 1.65 | −0.04 | −1.24 |

UAE | 0.02 | 2.52 | −0.09 | −2.43 | ||||

Kuwait | 0.70 | 0.33 | −0.08 | −1.10 | ||||

Oman | 0.02 | 0.79 | −0.07 | −0.82 | ||||

Bahrain | 0.02 | 5.39 | −0.06 | −3.39 | ||||

Qatar | 0.08 | 2.65 | −0.18 | −1.09 | ||||

Askari (2002) [27] | 1995–1999 | Iran | Residential | GLS | 0.11 | 0.16 | −0.97 | −1.36 |

Amini Fard and Estedlal (2003) [28] | 1967–2000 | Iran | Residential | ECM | 0.00 | 0.24 | 0.00 | −0.59 |

Atakhanova and Howie (2007) [29] | 1994–2003 | Kazakhstan | Aggregate | Panel GMM | 0.72 | Not reported | 0.00 | Not reported |

Industrial | 0.78 | Not reported | 0.00 | Not reported | ||||

Services | 0.75 | Not reported | −0.12 | Not reported | ||||

Residential | 0.12 | 0.59 | −0.22 | −1.10 | ||||

Eltony and Al-Awadhi (2007) [30] | 1975–2003 | Kuwait | Commercial | ECM | 0.34 | 0.50 | −0.33 | −1.64 |

Eltony and Al-Awadhi (2007) [31] | 1975–2005 | Kuwait | Residential | ECM | 0.18 | 0.31 | −0.23 | −0.56 |

(Energy demand actually modeled, but consists predominately of electricity.) | ||||||||

Pourazarm and Cooray (2013) [32] | 1967–2009 | Iran | Residential | ARDLBT | 0.04 (But insignificant) | 0.58 | −0.03 (But insignificant) | 0.00 |

Atalla and Hunt (2016) [33] | 1985–2012 | Saudi Arabia | Residential | STSM | 0.00 | 0.48 | −0.10 | −0.10 |

Oman | 0.72 | 0.86 | −0.09 | −0.10 | ||||

Kuwait | 0.30 | 0.43 | 0.00 | 0.00 | ||||

Bahrain | 0.00 | 0.71 | 0.00 | 0.00 | ||||

(Qatar and UAE results are omitted since estimated equations were poorly specified.) |

$\mathit{E}$ | $\mathit{P}$ | $\mathit{G}\mathit{D}\mathit{P}$ | $\mathit{G}\mathit{D}\mathit{P}\mathit{T}$ | $\mathit{H}\mathit{D}\mathit{D}$ | $\mathit{C}\mathit{D}\mathit{D}$ | |
---|---|---|---|---|---|---|

Mean | 1.76 | 0.03 | 970.97 | 1653.42 | 14,690.97 | 18,432.05 |

Median | 1.77 | 0.02 | 799.07 | 1125.45 | 14,563.70 | 18,378.19 |

Maximum | 2.31 | 0.05 | 2053.05 | 3376.73 | 16,143.83 | 19,973.30 |

Minimum | 1.35 | 0.02 | 357.23 | 397.81 | 12,821.17 | 16,511.11 |

Standard Deviation | 0.25 | 0.01 | 551.69 | 1132.71 | 734.86 | 769.38 |

Variable | The ADF Test | The PP Test | |||||||
---|---|---|---|---|---|---|---|---|---|

Test Value | C | t | None | k | Test Value | C | t | None | |

$e$ | −2.992 | x | x | 2 | −1.767 | x | x | ||

$p$ | −2.851 | x | x | 0 | −2.851 | x | x | ||

$gdp$ | −2.675 | x | x | 1 | −2.497 | x | x | ||

$hdd$ | −4.737 *** | x | 1 | −8.051 *** | x | ||||

$cdd$ | −5.081 *** | x | 1 | −10.460 *** | x | ||||

$\Delta e$ | −3.569 *** | x | 0 | −3.607 *** | x | ||||

$\Delta p$ | −5.109 *** | x | 0 | −5.370 *** | x | ||||

$\Delta gdp$ | −3.987 *** | x | 1 | −3.052 * | x |

Panel A: Serial Correlation LM Test ^{a} | |||||

Lags | LM-Statisticb | Probability | |||

1 | 2.572 | 0.979 | |||

2 | 13.550 | 0.139 | |||

3 | 4.174 | 0.900 | |||

Panel B: Normality Test ^{b} | |||||

Statistic | ${\chi}^{\mathbf{2}}$ | d.f. | Probability | ||

Skewness | 4.827 | 3 | 0.185 | ||

Kurtosis | 3.366 | 3 | 0.339 | ||

Jarque-Bera | 8.193 | 6 | 0.224 | ||

Panel C: Heteroscedasticity Test ^{c} | |||||

White | ${\chi}^{\mathbf{2}}$ | d.f. | Probability | ||

Statistic | 81.226 | 78 | 0.379 | ||

Panel D: Johansen Cointegration Test Summary | |||||

Data Trend: | None | None | Linear | Linear | Quadratic |

Test Type: | (a) No C and t | (b) Only C | (c) Only C | (d) C and t | (e) C and t |

Trace: | 1 | 1 | 1 | 1 | 1 |

Max-Eig: | 1 | 2 | 1 | 1 | 1 |

Panel E: Johansen Cointegration Test Results for Type c | |||||

Null Hypothesis: | r = 0 | r ≤ 1 | r ≤ 2 | ||

λ_{trace} | 41.065 ** | 11.049 | 0.635 | ||

λ^{a} _{trace} | 31.403 ** | 8.449 | 0.485 | ||

λ_{max} | 30.016 ** | 10.414 | 0.635 | ||

λ^{a} _{max} | 22.953 ** | 7.964 | 0.485 |

**The null hypothesis in the Serial Correlation LM Test is that there is no serial correlation at lag order h of the residuals;**

^{a}**System normality test with the null hypothesis of the residuals are multivariate normal;**

^{b}**White Heteroscedasticity Test takes the null hypothesis of no cross terms heteroscedasticity in the residuals; ${\mathsf{\chi}}^{2}$ is Chi-squared; d.f. means degree of freedom;**

^{c}**C**and

**t**indicate intercept and trend. r is rank of $\mathsf{\Pi}$ matrix, i.e., number of cointegrated equations; λ

_{trace}and λ

_{max}are the Trace and Max-Eigenvalue statistics, while λ

^{a}

_{trace}and λ

^{a}

_{max}are adjusted version of them; ** denotes rejection of the null hypothesis at the 5% significance level; Critical values for the cointegration test are taken from [93]; Estimation period: 1997–2013. As [94] shows, when a pulse dummy is included in a VAR a blip dummy (which is equivalent to the change in the pulse dummy) should be included in the VECM, which is the procedure followed here.

Panel A: Statistics for Testing the Significance of a Given Variable in the Cointegrating Space ^{a} | |||

E | p | gdp | |

χ^{2} (1) | 17.774 *** | 14.688 *** | 18.714 *** |

Panel B: Multivariate Statistics for Testing Stationarity ^{b} | |||

E | p | gdp | |

χ^{2} (2) | 22.129 *** | 29.215 *** | 26.452 *** |

Panel C: Weak Exogeneity Test Statistics ^{c} | |||

E | p | gdp | |

χ^{2} (1) | 19.152 *** | 0.002 | 3.499 * |

^{a}the null hypothesis is that given variable is statistically insignificant;

^{b}the null hypothesis is that given variable is (trend) stationary;

^{c}the null hypothesis is that given variable is weakly exogenous; * and *** denote rejection of the null hypotheses at the 10% and 1% significance levels, respectively; Estimation period: 1997–2013.

Methods | $\mathit{p}$ | $\mathit{g}\mathit{d}\mathit{p}$ | Intercept |
---|---|---|---|

Coef. (Std. Er.) | Coef. (Std. Er.) | Coef. (Std. Er.) | |

VECM | −0.950 (0.084) *** | 0.191 (0.031) *** | −4.148 (0.562) *** |

ARDLBT | −0.994 (0.094) *** | 0.204 (0.034) *** | −4.401 (0.536) *** |

DOLS | −0.894 (0.078) *** | 0.174 (0.028) *** | −3.909 (0.425) *** |

FMOLS | −0.788 (0.118) *** | 0.143 (0.052) *** | −3.264 (0.717) *** |

CCR | −0.984 (0.145) *** | 0.207 (0.059) *** | −4.398 (0.864) *** |

Method | Johansen | ARDLBT | DOLS | CCR | FMOLS |
---|---|---|---|---|---|

Panel A: The final ECM Specifications | |||||

Regressor | Coef. (Std. Er.) | Coef. (Std. Er.) | Coef. (Std. Er.) | Coef. (Std. Er.) | Coef. (Std. Er.) |

$ect\_Jo{h}_{t-1}$ | −0.921 (0.100) *** | - | - | - | - |

$ect\_ARDLB{T}_{t-1}$ | - | −0.879 (0.097) *** | - | - | - |

$ect\_DOL{S}_{t-1}$ | - | - | −0.969 (0.108) *** | - | - |

$ect\_CC{R}_{t-1}$ | - | - | - | −0.892 (0.098) *** | - |

$ect\_FMOL{S}_{t-1}$ | - | - | - | - | −1.025 (0.140) *** |

$c$ | −0.039 (0.028) | −0.022 (0.028) | 0.026 (0.026) | −0.009 (0.027) | −0.038 (0.035) |

$\Delta {p}_{t}$ | −0.367 (0.044) *** | −0.373 (0.045) *** | −0.356 (0.045) *** | −0.372 (0.045) *** | −0.324 (0.051) *** |

$\Delta {p}_{t-1}$ | 0.328 (0.051) *** | 0.330 (0.052) *** | 0.321 (0.052) *** | 0.331 (0.053) *** | 0.295 (0.061) *** |

$\Delta gd{p}_{t-1}$ | 0.483 (0.285) | 0.402 (0.283) | 0.580 (0.299) * | 0.412 (0.285) | 0.707 (0.371) * |

Panel B: Statistics, Residuals Diagnostics and Misspecification tests results | |||||

$\widehat{\mathsf{\delta}}$ | 0.0331 | 0.0335 | 0.0339 | 0.0336 | 0.0403 |

AIC | −3.738 | −3.717 | −3.689 | −3.710 | −3.345 |

SBC | −3.493 | −3.472 | −3.444 | −3.465 | −3.100 |

${F}_{AR}$ | 0.196 [0.826] | 0.258 [0.777] | 0.064 [0.939] | 0.257 [0.778] | 0.260 [0.776] |

${F}_{ARCH}$ | 0.538 [0.475] | 0.516 [0.485] | 0.774 [0.394] | 0.420 [0.528] | 3.636 [0.077] * |

${F}_{HETR}$ | 0.333 [0.851] | 0.324 [0.857] | 0.405 [0.802] | 0.332 [0.851] | 0.936 [0.476] |

$J{B}_{N}$ | 3.957 [0.138] | 4.144 [0.126] | 2.554 [0.279] | 3.910 [0.142] | 1.259 [0.533] |

F_{FF} | 0.178 [0.681] | 0.146 [0.710] | 0.426 [0.528] | 0.221 [0.647] | 2.236 [0.163] |

Variable | Scenarios | ||
---|---|---|---|

“High“ Scenario | Business as Usual | “Low“ Scenario | |

Non-oil GDP, million AZN 2005 prices | 8% growth (Based on [105]) | 4% growth (Based on projections from [108]) | 2% growth (Based on projections from [106,107]) |

Population, millions | 1.0% growth (High Variant from [109]) | 0.7% growth (Medium Variant from [109]) | 0.5% growth (Low Variant from [109]) |

Consumer price index, %, 2005 = 100 | 8% growth (Based on projections from [106,110,111,112,113,114]) | 4% growth (Based on average growth rate of 2009–2013) | 2% growth (Based on projections from [106,110,111,112,113,114]) |

Electricity price, AZN per kwh | 0% growth (Based on making nominal prices unchanged) | 4% growth (Based on making real prices unchanged) | 17% increase in 2016, 34% increase in 2019 and 68% in 2022 |

Year | Population, Millions | Non-oil GDP, Million AZN in 2005 Price | Consumer Price Index, 2005 = 100 | Electricity Price, AZN per kwh | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

HS | BU | LS | HS | BU | LS | HS | BU | LS | HS | BU | LS | |

2014 | 9.54 | 9.54 | 9.54 | 20,789.71 | 20,789.71 | 20,789.71 | 185.53 | 185.53 | 185.53 | 0.06 | 0.06 | 0.06 |

2015 | 9.65 | 9.65 | 9.65 | 22,800.40 | 22,800.40 | 22,800.40 | 192.95 | 192.95 | 192.95 | 0.06 | 0.06 | 0.06 |

2016 | 9.88 | 9.87 | 9.86 | 24,624.43 | 23,712.41 | 23,256.41 | 208.39 | 200.67 | 196.81 | 0.06 | 0.06 | 0.07 |

2017 | 10.00 | 9.97 | 9.94 | 26,594.39 | 24,660.91 | 23,721.53 | 225.06 | 208.70 | 200.75 | 0.06 | 0.07 | 0.07 |

2018 | 10.12 | 10.07 | 10.02 | 28,721.94 | 25,647.35 | 24,195.97 | 243.06 | 217.05 | 204.76 | 0.06 | 0.07 | 0.07 |

2019 | 10.23 | 10.16 | 10.09 | 31,019.69 | 26,673.24 | 24,679.88 | 262.51 | 225.73 | 208.86 | 0.06 | 0.07 | 0.09 |

2020 | 10.34 | 10.24 | 10.14 | 33,501.27 | 27,740.17 | 25,173.48 | 283.51 | 234.76 | 213.04 | 0.06 | 0.07 | 0.09 |

2021 | 10.44 | 10.32 | 10.19 | 36,181.37 | 28,849.78 | 25,676.95 | 306.19 | 244.15 | 217.30 | 0.06 | 0.08 | 0.09 |

2022 | 10.53 | 10.38 | 10.23 | 39,075.88 | 30,003.77 | 26,190.49 | 330.69 | 253.91 | 221.64 | 0.06 | 0.08 | 0.16 |

2023 | 10.62 | 10.44 | 10.27 | 42,201.95 | 31,203.92 | 26,714.30 | 357.14 | 264.07 | 226.07 | 0.06 | 0.08 | 0.16 |

2024 | 10.70 | 10.50 | 10.30 | 45,578.10 | 32,452.08 | 27,248.59 | 385.71 | 274.63 | 230.60 | 0.06 | 0.09 | 0.16 |

2025 | 10.78 | 10.55 | 10.32 | 49,224.35 | 33,750.16 | 27,793.56 | 416.57 | 285.62 | 235.21 | 0.06 | 0.09 | 0.16 |

Statistic | Final ECM Specifications | ||||
---|---|---|---|---|---|

Johansen | ARDLBT | DOLS | CCR | FMOLS | |

Root Mean Squared Error | 0.410 ^{##} | 0.422 | 0.417 ^{#} | 0.422 | 0.502 |

Mean Absolute Error | 0.316 ^{##} | 0.325 | 0.320 ^{#} | 0.327 | 0.430 |

Mean Abs. Percent Error | 2.108 ^{##} | 2.126 ^{#} | 2.183 | 2.147 | 2.891 |

Theil Inequality Coefficient | 0.013536 ^{##} | 0.013924 | 0.013785 ^{#} | 0.013910 | 0.016605 |

Bias Proportion | 0.000210 | 0.000007 ^{#} | 0.001382 | 0.000002 ^{##} | 0.005110 |

Variance Proportion | 0.001463 ^{##} | 0.011423 | 0.059960 | 0.006865 ^{#} | 0.338458 |

Covariance Proportion | 0.998327 ^{##} | 0.988570 | 0.938658 | 0.993133 ^{#} | 0.656431 |

^{##}indicates the “best model” and

^{#}the “second best model” as suggested by the given statistics where the smallest value indicates the best model other than for the Covariance Proportion where the best model is given by the largest value for the statistic.

Method | Scenario | 2013 | 2015 | 2020 | 2025 |
---|---|---|---|---|---|

ARDLBT | HS | 15.985 | 17.390 | 27.761 | 45.542 |

BU | 15.985 | 17.390 | 19.550 | 20.802 | |

LS | 15.985 | 17.390 | 14.707 | 8.929 | |

CCR | HS | 15.985 | 17.461 | 27.832 | 45.523 |

BU | 15.985 | 17.461 | 19.626 | 20.893 | |

LS | 15.985 | 17.461 | 14.783 | 9.032 | |

DOLS | HS | 15.985 | 17.097 | 26.317 | 41.149 |

BU | 15.985 | 17.097 | 18.941 | 20.069 | |

LS | 15.985 | 17.097 | 14.514 | 9.332 | |

FMOLS | HS | 15.985 | 16.802 | 24.798 | 36.844 |

BU | 15.985 | 16.802 | 18.393 | 19.400 | |

LS | 15.985 | 16.802 | 14.447 | 9.848 | |

Johansen | HS | 15.985 | 17.270 | 27.145 | 43.608 |

BU | 15.985 | 17.270 | 19.284 | 20.483 | |

LS | 15.985 | 17.270 | 14.616 | 9.101 | |

HS MAX | 15.985 | 17.461 | 27.832 | 45.542 | |

BU MAX | 15.985 | 17.461 | 19.626 | 20.893 | |

BU MIN | 15.985 | 16.802 | 18.393 | 19.400 | |

LS MIN | 15.985 | 16.802 | 14.447 | 8.929 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hasanov, F.J.; Hunt, L.C.; Mikayilov, C.I.
Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques. *Energies* **2016**, *9*, 1045.
https://doi.org/10.3390/en9121045

**AMA Style**

Hasanov FJ, Hunt LC, Mikayilov CI.
Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques. *Energies*. 2016; 9(12):1045.
https://doi.org/10.3390/en9121045

**Chicago/Turabian Style**

Hasanov, Fakhri J., Lester C. Hunt, and Ceyhun I. Mikayilov.
2016. "Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques" *Energies* 9, no. 12: 1045.
https://doi.org/10.3390/en9121045