31 electricity needs in Libya should not be less than consumption. If that happens, it
will shake up the Libyan economy. Besides that, attention should be paid to that the electric power will be lost, as long as we have not resolved the bias in
producing electricity, should pay attention to power consumption and loss of electricity, or in other words to meet the demands for electricity in producing
electricity it needs to accumulate between electricity consumption and electricity loss.
4.3.6 Production cost in the process of distributing electricity to Libya
Production costs for producing electricity to be distributed to consumers as in the Table 4.11.
Table 4.11 Real and Forecasting of production Cost by SPSS Years
Real of Production cost MWh
Years Forecasting of
Production cost MWh 2000
235,846,163.4 2011
537,150,947 2001
246,466,479.2 2012
571,653,578 2002
269,796,995.9 2013
606,156,210 2003
291,526,567.8 2014
640,658,842 2004
310,905,609.7 2015
675,161,473 2005
345,503,222.3 2016
709,664,105 2006
369,243,682.4 2017
744,166,737 2007
392,661,722 2018
778,669,368 2008
441,171,910 2019
813,172,000 2009
468,261,988.2 2020
847,674,632 2010
500,655,952.9 2021
882,177,264 2022
916,679,895
4.3.7 Forecasting of electricity demand to determine the long-term from
2011-2022
Forecasting electricity to meet the demand in Libya from year 2011-2022 as Table 4.12 and 4.13.
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32 Table 4.12 Forecasting of electricity demand
Years Generated
MWh Consumption
MWh 2011
34,902,596 33,396,572
2012 37,144,482
35,112,440 2013
39,386,368 36,828,308
2014 41,628,255
38,544,176 2015
43,870,141 40,260,043
2016 46,112,028
41,975,911 2017
48,353,914 43,691,779
2018 50,595,800
45,407,647 2019
52,837,687 47,123,515
2020 55,079,573
48,839,383 2021
57,321,460 50,555,251
2022 59,563,346
52,271,119 Source: analysis data by SPSS 15 version 2014
Table 4.13 Growth forecasting of electricity demand Years
Generated MWh
Consumption MWh
2011 6.44
7.24 2012
6.42 5.14
2013 6.04
4.89 2014
5.69 4.66
2015 5.39
4.45 2016
5.11 4.26
2017 4.86
4.09 2018
4.64 3.93
2019 4.43
3.78 2020
4.24 3.64
2021 4.07
3.51 2022
3.91 3.39
Source: analysis data by SPSS 15 version 2014 perpustakaan.uns.ac.id
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4.3.8 The Mathematical Model is Based On Forecasting
Formula of forecasting: Q = a + b × a × y 4.1
Where, a
= amount at the initial year b
= coefficient y
= difference between forecasting year target and the initial year Example: - mathematical forecasting of POP for 2017
y = 2017-2000 = 17 The increase in the amount of fuel consumption 2000-2010
Ratio of population = 2010-2000
= 6,355,112-5,231,189 =1123923
= 112392310 = 112392.3
Increase of population
100 population
of Ratio
year initial
the at
Population Q
100 231189
, 5
112392.3
Q = 2.1485 or 0.0214
Q = 5,231,189 + 0.0214 5,231,189 y
Q = 2,349,614 + 0.0789 x 2,349,614 x 17
Q = 7,134,295
Error if compare with result of forecasting of SPSS
100 2017
in SPSS
by g
Forecastin EViews
or Formula
by g
forecastin -
2017 in
SPSS by
g Forecastin
Q
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4.3.9 Calculation Exponential Smoothing
