38 Table 4.20 Error Calculation Of Moving Average Consumption
Years Consumption
Actual Data Forecast MA=4
Absolute deviation
Forecast MA=5 Absolute
deviation 2000
14,522,025.00 2001
15,208,543.00 2002
15,445,816.00 2003
16,702,230.00 2004
17,931,957.00 15,469,653.50
2,462,303.50 2005
19,909,502.00 16,322,136.50
3,587,365.50 15,962,114.20
3,947,387.80 2006
21,730,193.00 17,497,376.25
4,232,816.75 17,039,609.60
4,690,583.40 2007
23,016,749.00 19,068,470.50
3,948,278.50 18,343,939.60
4,672,809.40 2008
27,748,242.00 20,647,100.25
7,101,141.75 19,858,126.20
7,890,115.80 2009
29,427,305.00 23,101,171.50
6,326,133.50 22,067,328.60
7,359,976.40 2010
31,680,704.00 25,480,622.25
6,200,081.75 24,366,398.20
7,314,305.80 2011
27,968,250.00 26,720,638.60
MAD 2,796,825
2,672,064
Table 4.21 Error Calculation Of Moving Average Population
Years Population
Actual Data Forecast MA=4
Absolute deviation
Forecast MA=5 Absolute
deviation 2000
5,231,189.00 2001
5,331,311.00 2002
5,434,293.00 2003
5,541,062.00 2004
5,652,797.00 5,384,463.75
268,333.25 2005
5,769,709.00 5,489,865.75
279,843.25 5,438,130.40
331,578.60 2006
5,893,738.00 5,599,465.25
294,272.75 5,545,834.40
347,903.60 2007
6,023,053.00 5,714,326.50
308,726.50 5,658,319.80
364,733.20 2008
6,149,620.00 5,834,824.25
314,795.75 5,776,071.80
373,548.20 2009
6,262,667.00 5,959,030.00
303,637.00 5,897,783.40
364,883.60 2010
6,355,112.00 6,082,269.50
272,842.50 6,019,757.40
335,354.60 2011
6,197,613.00 6,136,838.00
MAD 619,761
613,684
The describ in detail to calculate the error of forecasting in 2022. Next, the calculate error forecasting uses Moving Average and make sure uses MAD.
4.3.12 Calculation of ARIMA
The first column is time or period. The second column is t period or time. The third column are actual data.
1. The first step input actual data 2. Find forecasting with averages then use formulation moving averages
commit to user
39 MA =
N A
A A
N t
t t
1 1
......
4.3
A
t
= Actual in t period N
= count data are needed 3. Until here, calculate about forecasting was finish, and the next step, analysis to
make sure forecasting used Mean Absolute Deviation MAD 4. so, the Calculate the error actual data
– forecasting data 5. Change the error by absolute data, the absolute error is move symbol front of
numbers 6. Cumulative is calculation absolute error
7. The mad calculate is number cumulative in the last number divided count of all period
The last step is calculated the mad and low value it is will be better model to forecasting. Finally the best model.
Table 4.22 Comparison MAD Calculation
MAD ARIMA MA=4
MAD ARIMA MA=5
MAD Exponential Smoothing
Conclution Production
45,068,789 43,439,905
35,956,177 Exponential Smoothing
Generated 2,928,446
2,822,606 2,336,334
Exponential Smoothing Consumption
2,796,825 2,672,064
2,248,789 Exponential Smoothing
Population 619,761
613,684 157,288
Exponential Smoothing
Calculate the error to make a sure of result if compare with result of forecasting of SPSS or EViews untill 2022 year by year from 2011-2022 by this formultion:
100 SPSS
by g
Forecastin EViews
or Formula
by g
forecastin -
SPSS by
g Forecastin
Q 4.5
Example: error ratio between manual Excel and SPSS perpustakaan.uns.ac.id
commit to user
40 Table 4.23 Error ratio between manual Excel and SPSS
Years Production Generated Consumption Population
2011 1,89
1,89 0,02
0,31 2012
3,25 3,25
0,02 0,59
2013 4,48
4,48 0,02
0,85 2014
5,61 5,61
0,02 1,09
2015 6,64
6,64 0,02
1,31 2016
7,59 7,59
0,02 1,51
2017 8,46
8,46 0,02
1,70 2018
9,27 9,27
0,03 1,87
2019 10,03
10,03 0,03
2,03 2020
10,73 10,73
0,03 2,17
2021 11,38
11,38 0,03
2,30 2022
11,99 11,99
0,03 2,41
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41
CHAPTER V CONCLUSION AND RECOMENDATION
5.1 Conclusion
Based on the research and the result of analysis about forecasting of the electricity demands in Libya using time series Stochastic method for long-term
from 2011-2022, we can conclude the following: We were able to determine the long-term demands for electricity in Libya
in term of data from 2000-2010, by using forecasting time series. Time series forecasting method is formulated with time series data. Technical time series:
there are two time series, they are deterministic and stochastic time series. Time series deterministic is a forecasting method with time series data where the data
must meet the data stationary. This is due to a deterministic time series forecasting are methods appropriately. Terms of stationary data are used as the basis for
forecasting the proper method deterministic time series. Studying energy demands at a disaggregated sectoral level for policy purposes. An application to the demand
for electricity in Libya shows that alternative policy options for market reforms can be based on reliable long-term forecasts from in-sample parameter estimates
of long-run relationships that are useful for decision-making To provide mathematical data that can be used as consideration in deciding
a particular policy in the field of electricity supply, mathematical data that can be used in A stochastic time series is forecasting method assuming there is still a
possibility of committing a mistake. The data used for the analysis of stochastic time series does not require stationary requirements.
The research analysis process is a stochastic time series analysis because the data used does not meet the requirements of stationary. On the results of
stochastic time series analysis using SPSS, the process runs smoothly. Models found in the SPSS analysis is ARIMA. The results of the analysis of SPSS can be
presented. As for the mathematical models that can be used as a material
consideration in deciding a particular policy in the field of power supply, perpustakaan.uns.ac.id
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