International Journal of Research In Vocational Studies (IJRVOCAS)
Vol. 2 No. 4 (2023): IJRVOCAS – Special Issues – INCOSTIG – PP. 13~19 Print ISSN 2777-0168| Online ISSN 2777-0141| DOI prefix: 10.53893 https://journal.gpp.or.id/index.php/ijrvocas/index
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Assessment of Grey Forecasting Model: Study Case for Electrification Rate in Indonesia from 2012 to 2021
Mufrida Zein, Muhammad Ghalih*, Rina Pebriana
Department of Computer and Business, Politeknik Negeri Tanah Laut, Pelaihari, Indonesia
ABSTRACT
In 2021, Indonesia was 99.45% electrified. That year's aim was 100%. Due of Indonesia's 17,000 islands, electrifying rural settlements is tough. Depending on network size and demand, Indonesia's energy mix varies, however it often includes coal. After adopting the Paris Climate Agreement, Indonesia vowed to increase renewable energy to 23% by 2025. Indonesia's renewable energy production has increased. The government expects coal to be important in coming decades. The GM (1, 1) model of Grey theory was used to estimate Indonesia's electrification rate from 2012 to 2021. The model's average residual error is above 5%, according to the calculation. Indonesia's electrification rate is expected to grow annually. According to the trials, the recommended technique boosts the forecasting accuracy of the original Grey models and gives Indonesia a helpful reference for designing the action plan.
Keywords:
Electrification Forecasting Grey Theory GM (1, 1) Indonesia
Corresponding Author:
Muhammad Ghalih,
Department of Computer and Business, Politeknik Negeri Tanah Laut,
Akhmad Yani Road Km. 6, Panggung, Pelaihari, Tanah Laut, Kalimantan Selatan, Indonesia.
Email: ghalih@politala.ac.id
1. INTRODUCTION
The process of electrifying entails providing power to a system by means of the application of electricity, and in many instances, putting this power into effect involves transitioning from an earlier source of power. The use of electrical power is another viable method for putting this power into action. Electrifying certain economic sectors is known by a number of titles, including the electrification of industries, electrification of households, electrification of rural areas, and electrification of railroads. Each of these names refers to a different aspect of the process. These terms, taken individually, each explain a separate facet of the procedure. In addition, it may refer to the process of converting traditionally coal- or coke-heated industrial operations, such as melting, smelting, separating, or refining, to an electric process, such as converting to an electric arc furnace, electric induction or resistance heating, electrolysis, or electrolytic separating. Historically, these operations have been carried out to melt, smelt, separate, or refine metals. In this context, the word
"electric" refers to the transition from a non-electric process into an electric one.
Due to the country's topography, it is difficult to construct new power plants in Indonesia and much more challenging to expand the ones that are already there. It is necessary to link the networks of a substantial number of islands in order to provide coverage over the whole of the country. Bringing electricity to remote rural villages in Indonesia, especially those located in the eastern half of the country, continues to be a significant challenge from a logistical standpoint. In addition to the roughly 600 smaller and more scattered
INCOSTIG International Conference on Science, Technology and Engineering 2022
electrical grids. The majority of the networks are administered and operated by PLN, which is a government- owned electrical utility corporation. PLN is responsible for the majority of the networks. The vast majority of networks are likewise under PLN's control.
A longitudinal study by Deng[1], [2] and [3]reported that the first systematic study of Grey theory in 1982, which has been recognized and applied by many academicians in different subjects such as economic [4] and business [5],[6] and [7] energy [8], electrical power [9], technological progress [10], engineering [11], [12] and agriculture [13], chosen Grey prediction as an ability forecasting means because of having relatively low data requirements, and a GM model constructed from a sample of just four pieces of data. In addition to that forecast method is significant by using the transformed Grey rolling modeling mechanism. This rolling modeling mechanism provides a means to guarantee input data are always the most recent values from time series data to forecast the number to get the result. The present paper examines the Grey forecasting method of electrification rate in Indonesia, during 10 year since 2012 until 2021.
The first section of this piece is a description of the prior research that has been done on the application of the Grey forecasting technique to a range of various disciplines of study. The research that has been done can be found in the references section of this article. There was the potential for the grey forecasting approach to be used in a range of different domains. The next part of the study shows not only the method, but also the data and the empirical outcomes for each observation period. This phase comes after the first part of the investigation. The data from the forecast results and the rolling modeling data are introduced with the use of an expression. This formula illustrates the degree to which their average residual error deviates from the data provided by GM for rolling models (1, 1). This disparity might be due to the methodology that was used in the study to get the most precise estimates of the electrification rate. These forecasts are being made available to Indonesia in order to provide that country with a point of reference for the purpose of establishing an action plan for the future.
The authors discuss not only the findings of the research but also the enhancements that were made to improve the accuracy of the forecasts made by the original Grey models in the section that comes at the end of the publication. These enhancements were made to improve the accuracy of the predictions made by the original Grey models. These improvements were developed in order to get a higher level of precision in the forecasts that the first Grey models produced. In conjunction with the authors' discussion of the findings of the research, a number of potential enhancements were brought to the table for consideration. These numbers provide an important starting point for the pace of electrification in Indonesia and may be used as a foundation for the building of future action plans if the government so chooses.
2. RESEARCH METHOD
The Grey theory, developed by Deng[1] in 1982, has received a lot of attention in the literature because it may be used for short-term forecasting without using a statistical method. Additionally, several study fields, including finance, engineering, agriculture, and management, have effectively used the Grey forecasting method. In addition, the fundamental methodologies of the Grey system theory are used in Grey producing systems like Grey relational analysis, forecasts, decisions, and controllers. However, in this study, we primarily employ the transformed Grey rolling modeling mechanism to the forecasting approach. It is possible to ensure that the input data are always the most recent values attributable to this rolling modeling approach. In another major study Wang et al [14] this research applied the general GM (1, 1) [15].
As a result, Figure 1 depicts an expression introducing the comparison of rolling modeling data and essential information about prediction outcomes. The typical residual error for various rolling GM (1, 1) [16].
Choose the first four continuous data in Method 1 to forecast the fifth output value, the second to fifth consecutive data to forecast the sixth output value, and so on. In addition, Method 2 predicts the sixth value of production by using the first five consecutive data, the second to sixth consecutive data to predict the seventh output value, and so on. By evaluating the accuracy of the Grey forecasting model [17], the study also offers strategies for estimating[18] the volume of electrification rate in Indonesia with the greatest degree of accuracy.
A Grey model for forecasting was proposed by Deng [1] after a thorough investigation. Accumulated Generation Operation (AGO): Accumulating time-series data that have been obtained with systematic regularity.
x( )0 =(x(0)(1),x(0)(2),...,x(0)( ))n (1) x(1) is x(0) one-order accumulated generating operation (AGO) sequence, that is,
1 (0) 2
(1) (0) (0)
1 1 1
( ( ), ( ),..., ( ))
n
k k k
x x k x k x k
= = =
=
(2)https://doi.org/10.53893/ijrvocas.v2i4.157
Inverse-accumulated generating operation (IAGO):
(0)
(1) (1)
( ) ( ) ( 1)
x k x k x k
= − − Gray Derivatives.
(1) (1) (1)
0.5 ( ) 0.5 ( 1)
z = x k + x k− (3) Gray Difference Equation Derivatives. The first order differential equation of GM(1, 1) model is /
dx dt+ax=b, where tdenotes the independent variables in the system, arepresents the developed coefficient, bis the Grey controlled variable, moreovera and b denoted the parameters requiring determination in the model. When a model is constructed, the differential equation is x0( )k +az(1)( )k =b, including k=2,3,...,n, where ,a bdenoted standby substantial number, this differential equation
(0) (1)
( ) ( )
x k +az k =bis called as GM (1, 1) model.
, T T , ( T ) 1 T
N N N
Y =BA B Y =B BA A= B B − B Y
Furthermore, accumulated matrix aand bare as below expand equations:
(1) (0) (1) (0)
2 2 2
2
(1) 2 (1)
2 2
( ) ( ) ( 1) ( ) ( )
( 1) ( ) ( )
n n n
k k k
n n
k k
z k x k n z k x k
a
n z k z k
= = =
= =
− −
=
− −
(4)
(1) 2 (0) (1) (1) (0)
2 2 2 2
(1) 2 (1) 2
2 2
[ ( )] ( ) ( ) ( ) ( )
( 1) [ ( )] [ ( )]
n n n n
k k k k
n n
k k
z k x k z k z k x k
b
n z k z k
= = = =
= =
−
=
− −
(5)Whitening Equation:
(1) (1) (1) ( 1)
( ) (1) b a ak b (1) b a k b
x k x e e x e
a a a a
− − −
= − + = − +
(1) (0)
( 1) (1) b ak b
x k x e
a a
−
+ = − + , wherex(1)(1)=x(0)(1).
Utilize Inverse-accumulated generating operation (IAGO) equation as below:
(0)
(1) (1) (0)
( 1) ( 1) ( ) (1 a) (1) b ak
x k x k x k e x e
a
+ = + − = − − − (6)
3. RESULTS AND ANALYSIS
According to the findings of the study, in order to provide a prediction on the overall rate of electrification in Indonesia, the Indonesian Ministry of Energy and Mineral Resources carried out an analysis of data series. This was done so that they could make an estimate. The findings of this investigation provide credence to the findings of a wide range of earlier investigations that had been carried out in this area of study before this inquiry was conducted. It is imperative that the real facts about the rate of electrification in Indonesia from the years 2012 through 2021 that are given in Table 2 be taken into account.
Table 2. Electrification rate in Indonesia from 2012 to 2021 (in %).
Year Real Data (in %) Grey Forecasting (GM 1, 1)
2012 76.56
2013 80.51
2014 84.35
2015 88.30
2016 91.16 92.46
2017 95.35 94.94
2018 98.30 98.88
2019 98.89 102.27
2020 99.20 101.08
2021 99.45 99.66
Source: Ministry of Energy and Mineral Resources (Indonesia) – January 2022
INCOSTIG International Conference on Science, Technology and Engineering 2022
off from the actual data that it was collected. It was found that there was a discrepancy here. Figure 1 illustrates how different these two groups of results are from one another. The true data and the prediction that was derived via the use of grey forecasting are represented by the black and grey colors, respectively, in the line chart. If you use these colors, you should have a better understanding of this conclusion. In other words, the black color represents the real data, while the grey color represents the forecast that was made based on the data. In contrast to the prediction, which is shown using a grayscale, the actual facts are represented by the color black. Taking a look at Figure 1 makes it abundantly clear that the projection for the period spanning five to ten years will not change, and that the Grey forecasting approach showed a considerable correlation between the actual data and the Grey forecasting strategy. This is something that is glaringly obvious to anyone who looks at it. This is shown by the fact that the forecast is presented in the form of a graphic. This substantiates the claim that this is the situation. Given that the findings of the current research appear to be in agreement with those of the previous study, it is possible that the Grey forecasting approach can be utilized in order to estimate the electrification rate in Indonesia in the not too distant future. This is suggested by the fact that the similarities between the two sets of findings have been observed.
Figure 1. Rolling model for forecasting from 2012 to 2021.
An optimum number to forecast the total of electrification rate in Indonesia has been developed with
= 0.4 [7, 8, 16, 28, 31], data series length m = 4, and data series step = 1. Slightly worse prediction results are obtained with = 0.4, m =4 and = 10, that is with the prediction from the same month of the previous years. Such data on GM (1, 1) prediction of electrification rate in Indonesia from Table 2 are given in Table 3.
In addition, there are the results for a four years period. However, for the purpose of analysis in Table 3 we can see that the lower error is more than 5 in 4 years and it is also the error higher around more then 8 in 10 years.
According to the average residual error that indicate the data time series about total data electrification rate in Indonesia is suitable to use Grey forecasting method.
Table 3. Average residual error.
Year Total Error (%)
1-4 -
5 0.014311441
6 0.004282404
7 0.005971296
8 0.034219308
9 0.019050038
10 0.002508663
0 10 20 30 40 50 60 70 80 90 100
1 2 3 4 5 6 7 8 9 10
Real Data Predictive value
https://doi.org/10.53893/ijrvocas.v2i4.157
4. ASSESSMENT OF GREY FORECASTING MODEL
In the assessment of the GM (1, 1) approach, which was employed in this research to anticipate the amount of electrification rate in Indonesia from 2012 to 2021, we evaluated its performance. According to the findings that were provided, the overall accuracy of the forecasting model is higher than 85%[19]. As a result, it is abundantly obvious that the model has a high level of prediction validity and is an attractive target for projecting the overall rate of electrification in Indonesia. In addition, the result of the forecast indicates that the overall electrification rate in Indonesia will continue to rise, which will lead to the development of a fresh plan for road-mapping in the future. Furthermore, the result of the forecast indicates that the result can count the electrification rate from Indonesia in order to formulate a more effective plan for the future.
This also aligns with our earlier findings, which demonstrated that results can explain and forecast for example, in Method 1: choose first four continuous data to forecast the 5th of output value, 2nd to 5th consecutive data to forecast the 6th output value and thereafter, in Method 2: predict the 6th of output value by adopting first five consecutive data, 2nd to 6th consecutive data to forecast the 7th output value and henceforth[20]. As a direct consequence of the results, it can be seen from the results that the Grey forecasting model demonstrates the highest forecast accuracy and an average accurate rate at an average residual error that is almost over 95%. This is because the results show that the Grey forecasting model is the most accurate overall. The numbers that were just shown demonstrate that the forecasting model that was suggested works well[21]. The forecasting approach that makes use of the Grey rolling model is, thus, the strategy that provides the most accurate predictions [22] with regard to the movement of the overall electrification rate in Indonesia.
5. DISCUSSION AND FUTURE WORK
When it comes to modeling in the Grey system, the number 0.5 that is often produced from GM (1, 1) displays the best degree of accuracy[15]. This value may be found in many different places. Figure 1 illustrates that the conclusion of the process of calculating the total rate of electrification in Indonesia is reliant on the primary data. This is because the conclusion is derived from the data that was collected initially. This is due to the fact that the important data comprise the vast bulk of the available information. This is shown by the fact that the procedure that is being discussed is depicted in the image that is under discussion. Because the data from 2016 and 2017 are nearly identical with the exception of a few numbers, it is obvious that we need to make some minor adjustments to those numbers in order to get reliable results from the system, which cannot continue to run the same figures. Because of this, it is obvious that we need to make some minor adjustments to the numbers. As a result of this, it is quite evident that we are going to have to make a few alterations to those statistics.
Despite this, the parameter in GM (1, 1) [23], which was used to estimate the number of mistakes associated with the electrification rate, has values that vary anywhere from 5.2 all the way up to 8.43 in Table 2. On the other hand, the accuracy of the forecasting may, depending on the specifics of the situation, be significantly impacted by the data series that is used for the forecasting. This is something that should be taken into consideration. This is something that cannot be overlooked and must be taken into account. Should summarize, each data series needs to, generally speaking, correlate to a number of differentiating markers, such as those provided in Table 3. These signals have the potential to be used in order to be of assistance in identifying the data. When more study is conducted, there is a good chance that a new Grey forecasting model of the electrification rate in Indonesia will be able to be implemented successfully. Because further investigation must be done, this option cannot be ruled out. This choice is made with the presumption that it will, at some time in the future, be a workable alternative. As a consequence of this, the Grey forecasting technique will also, in the future with updated data, result in another piece of research that places an emphasis on the pace at which the population is being electrified.
6. CONCLUSION
The ability to anticipate data series in order to create future prediction numbers based on historical data is one of the most important aspects of the Grey approach to forecasting, which also counts as one of the approach's main attributes. This ability allows for the creation of future prediction numbers based on historical data, which is one of the most important aspects of the Grey approach to forecasting. This is not only one of the most vital elements of the Grey approach to forecasting, but it is also one of the most essential characteristics of the method as a whole. This study attempted to produce an assessment of the percentage of Indonesia's landmass that is presently supplied by an electrical grid by making use of the Grey system modeling technique and with the aid of experimental data. Both of these methods were used in order to accomplish this
INCOSTIG International Conference on Science, Technology and Engineering 2022 period ranging from one to four years had an overall accuracy of 5.2%.
As a direct result of the test that was performed, both the actual data and the Grey forecast technique suggest that the annual growth rate of the overall rate of electrification in Indonesia is increasing from one year to the next. This conclusion can be drawn from the fact that both sets of data point to the same conclusion. The fact that the examination was performed serves as the basis for drawing this decision. This outcome is a direct consequence of the fact that the experiment was really carried out. It is possible to arrive at the conclusion that the government of Indonesia and the private sector there ought to collaborate in order to formulate a strategy for the future that will involve the expansion of the electrical industry in all of its aspects. This is something that can be done, and it is something that should be done. Because it is feasible to reach this conclusion, this is something that may be inferred from the fact that it is possible to do so. This is a direct consequence of the recent surge in demand for coffee that has been seen all around the world. Coffee consumption has recently increased.
ACKNOWLEDGEMENTS
This research supported by the Department of Computer and Business, Politeknik Negeri Tanah Laut.
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How to Cite
Zein, M., Ghalih, M., & Pebriana, R. . (2023). Assessment of Grey Forecasting Model: Study Case for Electrification Rate in Indonesia from 2012 to 2021. International Journal of Research in Vocational Studies (IJRVOCAS), 2(4), 13–19.
https://doi.org/10.53893/ijrvocas.v2i4.157
