24 As for who has the highest MAPE lowest in the population variable with a value
of 0.071 MAE. Mean absolute error. Measures how much the series varies from its
level prediction models. MAE is the highest on the production variable value and the lowest 6,021,407.853 population variable with a value of 4,272.438
MaxAPE. Maximum Absolute Percentage Error. The largest estimated error, expressed as a percentage. MaxAPE highest in the variable value
consumption by 10.868 and the population with the lowest value of 0.125. MaxAE. Maximum Absolute Error. The largest estimated error. MaxAE
highest in variable production abaout 19,004,280.541 and the lowest values occurring in the population variable with a value of 7,839,250.
Normalized BIC. Normalized Bayesian Information Criterion. General measure overall model fit that tries to explain the complexity of the model.
Normalized BIC variables that have high production is a variable with a value of 32,426 and the lowest values occur in the population variable with a value of 17.
273.
4.2.3 Result Analysis ARIMA Model Parameters by SPSS
Result analysis on the variable production cost, power generated, consumption, population, only four variables included in the ARIMA model type.
The results of forecasting the variables that could use the model as follows ARIMA.
commit to user
25 Table 4.8
Result analysis forecasting with ARIMA model parameters Years
Production Cost
Generated MWh
Consumption MWh
Population Real data
2011 405,504,753
26,348,587 12,993,675
6,103,221 2012
533,397,023 34,658,676
20,602,217 6,154,623
2013 543,774,561
35,332,980 6,237,393
Forecasting 2011
537,150,947 34,902,596
33,396,572 6,448,248.21
2012 571,653,578
37,144,482 35,112,440
6,542,064.89 2013
606,156,210 39,386,368
36,828,308 6,636,576.65
2014 640,658,842
41,628,255 38,544,176
6,731,790.58 2015
675,161,473 43,870,141
40,260,043 6,827,713.79
2016 709,664,105
46,112,028 41,975,911
6,924,353.35 2017
744,166,737 48,353,914
43,691,779 7,021,716.36
2018 778,669,368
50,595,800 45,407,647
7,119,809.92 2019
813,172,000 52,837,687
47,123,515 7,218,641.11
2020 847,674,632
55,079,573 48,839,383
7,318,217.03 2021
882,177,264 57,321,460
50,555,251 7,418,544.77
2022 916,679,895
59,563,346 52,271,119
7,519,631.42
There was gap on data 2011-2012 with the result forecasting on 2011-2012. That happened because of war tragedy in Libya. As long 7 month, in libya have
internal conflict, so use electricity consumption can not be forecast as exactly.
4.2.4 Result analysis Exponential Smoothing Model Parameters by EViews
Exponential smoothing models are classified as either seasonal or non- seasonal. Seasonal models are only available if a periodicity has been defined for
the active dataset see Current periodicity below. The results of the analysis of the description of the variables showed that the variables included in the
Exponential Smoothing model electricity demand Parameters are variables, production cost, generated, consumption and population. The results of the
forecasting models type included in the model of exponential smoothing parameters as follows.
commit to user
26 Table 4.9. Result analysis forecasting with Exponential Smoothing Model
parameters by EViews Years
Production Cost Generated
MWh Consumption
MWh Population
2011 405,504,753
26,348,587 12,993,675
6,103,221 2012
533,397,023 34,658,676
20,602,217 6,154,623
2013 543,774,561
35,332,980 6,237,393
Forecasting 2011
537,336,336 34,914,642
34,728,817 6,447,557
2012 571,873,032
37,158,742 37,312,458
6,540,002 2013
606,409,729 39,402,841
39,896,099 6,632,447
2014 640,946,425
41,646,941 42,479,741
6,724,892 2015
675,483,121 43,891,041
45,063,382 6,817,337
2016 710,019,818
46,135,141 47,647,023
6,909,782 2017
744,556,514 48,379,241
50,230,665 7,002,227
2018 779,093,210
50,623,340 52,814,306
7,094,672 2019
813,629,907 52,867,440
55,397,948 7,187,117
2020 848,166,603
55,111,540 57,981,589
7,279,562 2021
882,703,299 57,355,640
60,565,230 7,372,007
2022 917,239,995
59,599,740 63,148,872
7,464,452 Source: Analysis data by EViews
There was gap on empirical data and forecasting. That happened because of war tragedy in Libya. As long 7 month, at libya have internal
conflict, so use electricity consumption can not be forecast as exactly.
4.3 Comparison of forecasting results
