LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 146 Electrical Daily Load Forecasting In Ramadhan Using Type-2 Fuzzy Logic In Sulselrabar System Marhatang a1 , Muhammad Ruswandi Djalal a2 , Herman Nauwir a3 , Sonong a4 a Energy Engineering, State Polytechnic of Ujung Pandang Jalan Perintis Kemerdekaan km.10, Makassar E-mail : 1 marhatang@gmail.com, 2 wandi@poliupg.ac.id, 3 hermannauwir@poliupg.ac.id, 4 sonong@poliupg.ac.id, Abstract This study discusses the daily electricity load forecasting 24 hours on 150 kV electric power systems sulselrabar. Forecasting electrical load requires the accuracy of the results with a small error. Peak load forecasting methods used to use smart methods Interval Type-1 Fuzzy Logic (IT1FL) and Interval Type-2 Fuzzy Logic (IT2FL) to predict the needs of the electrical load 1 Ramadan 2016. As input data, it was used load data from 2012 through 2016 for the same day each 1st of Ramadhan each year, and as comparative data, it was used actual load data 1, 2016. For the Ramadhan input variable, it was used two of the data Variation Load Difference (VLD Max) 2015 as an input variable X, VLD Max 2016 as an input variable Y. From the simulation results obtained highly accurate results where each method produces a very small error, where for methods of using IT1FL of 1.607778264% while using IT2FL by, 1.344510913%. Keywords: Type-1 Fuzzy Logic, Type-2 Fuzzy Logic, MAPE, Load Forecasting 1. Introduction Electric load forecasting is an important part of power system operation in order to achieve optimal planning in operation of the systems [1]. Load forecasting is covering short-term, medium-term and long-term load forecasting. Short-term load forecasting is required for controlling and scheduling the operation of power systems [2]. Medium and long-term load forecasting is required for maintenance, fuel purchases, plant development and planning of future distributions. Accurate load forecasting has a significant impact on the operation and production costs of electric utilities [3]. Research on load forecasting has spawned numerous papers and journals [4]. These publications have led to the development of various methods of forecasting. This method is classified into two categories: The classical approach (conventional method) and an artificial intelligence method. The classical approach is based on statistical methods, which cannot accurately represent the complex nonlinear relationship between the load and a series of factors such as daily and weekly rhythms of time that can lead to high error in load forecasting [4]. Artificial intelligence method has the ability to provide better performance when dealing with nonlinear data. The advantages of artificial intelligence method compared to conventional method are computational technique and simple algorithm, structural simplicity and high accuracy performance without having to solve any nonlinear equations into mathematical equations. Therefore, the author in this research discusses the hybrid method in the load forecasting, which is a suggestion of earlier researchers. Thus the method of interval type 2 fuzzy inference system is used in this research. Interval type-2 fuzzy inference system (IT2FIS) becomes a concern for short-term load forecasting because it has a simple concept and high-performance identification. IT2FIS is the formulation and mapping process from input to output using interval type 2 fuzzy logic [5-9]. One of the advantages of fuzzy logic is the knowledge and experience of experts can be easily used and applied. Interval Type-1 Fuzzy Logic and Interval Type-2 Fuzzy Logic is used in this research for load forecasting in Sulawesi Selatan, Tenggara dan Barat (Sulselrabar) system especially for 1 Ramadhan 2016. In the proposed method, we do not take environmental LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 147 factors as variable. The Sulselrabar electrical system is used because, this system has been growing, and requires further study on load forecasting. Several previous studies have been conducted and show satisfactory results [9-21]. 2. Research Methods The implementation of IT2FL for peak load forecasting on 1 Ramadhan 2016 is done by using three stages, namely the preparation stage (pre-processing), processing stage and final stage (post-processing) [4]. 2.1. Pre-Processing Preparation stage is the preparation of peak load data on 24 hours to look for load difference (LD), typical load difference (TLD), maximum weekdays (max WD) and variation load difference (VLD). Load difference (LD) for maximum load is a load difference within 4 days before the days which is given by [22]: ( ) ( ) ( ) 100 ( ) MAX MaxSD i MaxWD i LD i x MaxWD i   (1) ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( ) 4 WD WD WD WD i d i d i d i d MaxWD i         (2) MaxSD (i) is the peak load on a special day and maxWD is the average of maximum load 4 days before the days. Then, looking for a distinctive characteristic of a typical peak load or typical load difference (TLDMAX (i)) by averaging the peak load of similar LDMAX (i) in previous years. After that, calculating the variation load difference, which is the difference between Load Difference (LD) and Typical Load Difference (TLDMAX (i)) which can be seen by the following equation: max max max ( ) ( ) ( )VLD i LD i TLD i  (3) max max max max ( 1) ( 2) ( 3) ( ) 3 LD i LD i LD i TLD i       (4) Peak load data which is used to calculate Max WD and LD max is based on (1) and (2) equations respectively and the results are presented in Table 1 and 2. Table 1. Peak Load In 2016 WD(i)d-4 WD(i)d-3 WD(i)d-2 WD(i)d-1 MaxSD(i) 577.96 536.22 583.10 589.64 609.70 562.64 513.60 560.86 563.12 606.52 537.60 497.91 527.11 541.81 615.86 517.76 498.68 516.53 533.25 641.13 526.03 489.66 525.30 546.27 596.93 539.42 528.80 550.95 571.02 591.33 536.83 529.59 558.15 567.28 520.18 559.59 573.80 584.02 595.88 574.02 599.36 617.64 634.73 649.16 627.04 587.65 655.20 658.25 692.32 657.29 614.61 689.41 682.15 686.51 656.71 614.24 689.49 675.38 682.78 659.18 611.61 683.15 663.73 694.33 664.00 612.52 704.85 692.95 710.65 675.02 608.56 698.42 676.79 691.70 691.70 614.76 681.74 661.68 701.46 695.61 603.86 651.71 661.77 677.62 695.79 723.27 754.12 783.38 741.25 770.25 816.40 836.67 842.27 853.60 856.00 801.50 821.69 791.02 815.15 812.24 LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 148 767.76 792.92 772.03 817.63 793.92 700.07 733.94 705.36 782.02 759.78 636.80 662.42 663.73 769.47 694.37 580.44 610.82 615.25 680.07 628.03 2.2. Processing Fuzzyfication design of X and Y input is using IT2MF Editor. There are 11 membership functions is used [23], namely :  Negative Very Big (NVB), range : [-48 -48 -40 -32.5 -48 -48 -40 -28.5 -48]  Negative Big (NB), range : [-40.5 -32 -24.5 -36.5 -32 -20.5]  Negative Medium (NM), range : [-32.5 -24 -16.5 -28.5 -24 -12.5]  Negative Small (NS), range : [-24.5 -16 -8.5 -20.5 -16 -4.5]  Negative Very Small (NVS), range : [-16.5 -8 -2.5 -12.5 -8 2.5]  Zero (ZE), range : [-8.5 0 4.5 -4.5 0 8.5]  Positive Very Small (PVS), range : [-2.5 8 12.5 2.5 8 16.5]  Positive Small (PS), range : [4.5 16 20.5 8.5 16 24.5]  Positive Medium (PM), range : [12.5 24 28.5 16.5 24 32.5]  Positive Big (PB), range : [20.5 32 36.5 24.5 32 40.5]  Positive Very Big (PVB), range : [28.5 40 48 48 32.5 40 48 48 48] Examples of fuzzy rules can be seen in Table 2. Table 2. Fuzzy Rules No. Antecedent Consequent Rules X Y Z 1 NM PS PS 2 PVB NS PVB 3 NM PM PM 4 NM PB PB 5 NS PM PM 6 NS PS PS 7 NM ZE ZE 8 NM PVS PVS 9 NVB ZE ZE 10 NVB ZE ZE 11 NVB NVS NVS 12 NVB ZE ZE 13 NVB ZE ZE 14 NVB ZE ZE 15 NVB PVS PVS 16 NVB PVS PVS 17 NM PS PS 18 NM PVS PVS 19 NS PVS PVS 20 NS PVS PVS 21 NS PVS PVS 22 NS PVS PVS 23 NS ZE ZE 24 ZE ZE ZE 2.3. Post-Processing After getting VLDMAX forecasting value, then forecast load difference:       MAX MAX MAXForecast LD i Forecast VLD i TLD i  (5) Peak load forecasting can be calculated: LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 149 ' ( ( )) ( ) ( ) 100 MAX MAX ForecastLD xMaxWD i P i MaxWD i  (6) The smaller error obtained show the accuracy of the proposed method is higher. The absolute error can be expressed as follows: 100% forecast actual actual P P Error x P   (7) ' ( ) ( ) 100% ( ) MAX P i MaxSD i Error x MaxSD i   (8) The research flowchart is shown in the following figure. AND Operator Implementation of IT2FIS MIN Function & MAC Composition Implementation Calculate Defuzzifikasi Value using Kernik Mendel Algorithm Max Iteration? Forecast Results Structure Confirm End Yes No Start Input Load Data Build Antencedent (X,Y) & Consequent (Z) Get Antencedent (X,Y) & Consequent (Z) IT2FLS Membership Function for getting FOU Value Build Fuzzy Rule No Yes Figure 1. Flowchart IT2FL for Daily Peak Load Forecasting 3. Literature Review LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 150 3.1. Fuzzy Logic Type-2 The fuzzy type-2 set is a development of fuzzy type-1 which is re-defuzzy. The Fuzzy type-1 based-knowledge logic system is used to build the rules in an uncertainty fuzzy logic system (FLS). There are three reasons for uncertainty rules [6] : 1 Rules of antecedents and consequents can have different perception in different people. 2 Polling of group of experts on consequents is often different to the same rules as most experts do not agree on the rule. 3 The training data contains a lot of noise. Type-2 fuzzy sets have their own membership levels are fuzzy. Rankings on type-2 fuzzy set can be on the subset of secondary membership. Similar with FLS Type-1, FLS Type-2 is also included FIS membership functions and defuzzification. The difference is that before the defuzzification process there is type reduction process which has several methods; one of them is Kernik Mendel Algorithm (KMA). Interval Type-2 Fuzzy Logic (IT2FL) structure can be seen in Figure 2. Figure 2 shows the process of IT2FL from an input value of crisp x set into the output value of Y=f(x) equation. Fuzzifikasi Rule Base Defuzzifikasi Inference Engine Input Crisp X IT2 FSs Output Crisp Y IT2 FSs Type- Reducer T1FS Figure 2. Type-2 Fuzzy Logic System (T2FLS) Structure 3.2. Interval Type-2 Fuzzy Set An interval type-2 fuzzy set (IT2FS) is denoted à by the membership function with , its characteristic can be recognized on the following equation:       , 0.1 ,xx X x J x uA A Jx x u       % % (9) x is a primary variable; , secondary variable, have domain for each is primary membership. Uncertainty of is the combination primary membership (footprint of uncertainty). The equation can be seen as follows: ( {( , ); [0,1]}) x X FOU Jx x u u JA x      % U (10) Jx is an interval with the following equation: ( , ); ( ), ( )AAJx x u u x x    %% (11) From equation 2.5 FOU ( ) can be expressed by the equation: ( ( ), ( )) x A X A FOU xA x       %% % U (12) Where: = Primary membership of = Lower Membership Function (LMF) af = Upper Membership Function (UMF)of LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 151 ( )UMF A ( )FOU A u I Embedded FS x ( )LMF A Figure 3. FOU (dark color), LMF (dotted line), UMF (solid line) and Embedded FS (wavy line). 3.3. Interval Type-2 Fuzzy Membership Function Operations Operation on fuzzy interval type-2 set is almost the same as fuzzy type-1 set; but on the IT2FL logic system, the operation is performed on two intervals that are UMF (top) and LMF (below) at once. Operation on fuzzy interval type-2 membership function can be seen in Figure 4: 1 0.9 0.8 0.7 0 1 2 3 4 N (x) Input 1 Max- Min Max- Min 1 0.9 0.8 0.7 0 1 2 3 4 N (x) Output 1 Figure 4. Operation fuzzy set interval type-2 (IT2FL) LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 152 5 Barru20 Pangkep21 Bosowa6 Tello7 Tello lama 29 Maros 37 Bontoala 24 Tanjung Bunga 8 Sgmnsa 25 Talasa 26 TIP 9 Jeneponto 10 Bulukumba 11 Sinjai 27 Bone 12 Soppeng 13 Sengkang 14 Makale 15 Palopo28 Sidrap 3 Pare-Pare 2 Pinrang 17 Polmas 1 Bakaru 18 Majene 19 Mamuju 31 Tonasa 32 Mandai 33 Daya 16 Borongloe 34 Tello A35 Tello B 36 Barawaja 23 Pnkukkg 4 Suppa GGGG G G G G G G G GG G G G 22 Tello lamaII 30 PangkepII PLTD Mateko PLTA Tmanipi PLTD Arena PLTD Smnsa PLTD Pjlsang PLTGU Sengkang PLTD Malea PLTD Palopo PLTD Suppa PLTD Pare PLTA Teppo PLTA BakaruPLTU Barru PLTA Bili PLTD Tello PLTD Agreko Figure 5. Sulselrabar System [10] LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 153 Table 3. Establishment Of Rule Base For Input X in 1st Ramadhan 2016 Hours Variable VLD max Membership Function (μ) Set of NB NM PVS PS X 01.00 X -13.53477757 0.383694394 0.616305606 NM Y 7.837201199 0.0406997 0.9593003 PS Z 7.837201199 0.0406997 0.9593003 PS Table 4. Result Of Variable Calculations X, Y, Z on 1st Ramadhan 2016 Hours Input Set X Y Z X Y Z 1:00 -13.53477757 7.837201199 7.837201199 NM PS PS 2:00 38.15805202 -8.455067268 -8.455067268 PVB NS NS 3:00 -12.34897699 12.81561102 12.81561102 NM PM PM 4:00 -10.98277044 15.86782032 15.86782032 NM PB PB 5:00 -9.099179456 10.65770448 10.65770448 NS PM PM 6:00 -7.434924909 9.002263816 9.002263816 NS PS PS 7:00 -11.37068292 0.269638493 0.269638493 NM ZE ZE 8:00 -12.03990371 3.199737038 3.199737038 NM PVS PVS 9:00 -19.64995022 0.863689423 0.863689423 NVB ZE ZE 10:00 -19.60150714 1.756933675 1.756933675 NVB ZE ZE 11:00 -22.75197853 -2.897867872 -2.897867872 NVB NVS NVS 12:00 -20.16793366 -0.76171919 -0.76171919 NVB ZE ZE 13:00 -18.72397279 1.320215573 1.320215573 NVB ZE ZE 14:00 -23.01970881 0.154211021 0.154211021 NVB ZE ZE 15:00 -19.25924082 3.424255509 3.424255509 NVB PVS PVS 16:00 -18.30164779 4.941932377 4.941932377 NVB PVS PVS 17:00 -11.51601435 7.053862708 7.053862708 NM PS PS 18:00 -10.31446966 3.604203806 3.604203806 NM PVS PVS 19:00 -7.106373861 3.350674093 3.350674093 NS PVS PVS 20:00 -7.094262262 4.663216896 4.663216896 NS PVS PVS 21:00 -6.418655252 2.380709895 2.380709895 NS PVS PVS 22:00 -9.138939847 2.765256248 2.765256248 NS PVS PVS 23:00 -7.372420856 1.397545706 1.397545706 NS ZE ZE 0:00 1.060461042 1.539682191 1.539682191 ZE ZE ZE Antecedent (X, Y) and consequent (Z) T2FIS figures as follows: Figure 6. Design System LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 154 Figure 7. X,Y Input Design Figure 8. Z Output Design 4. Result & Analysis The calculation of the input variable value X, Y, Z is to find the value of Load Difference Variable (VLDMAX) by first calculating WD Max, LD Max, TLDmax each input data of 2012-2015, which is calculated based on equation 1-4. The results of the calculation of variables X, Y, Z can be seen in table 3 above. Figure 5 shows the single line diagram of the sulselrabar system, where there are 37 Buses, each serving load centers in the sulselrabar system. Table 3 shows an example of the calculation of the membership function fuzzy logic for 01.00 hours, and Table 4 shows the complete result of the membership function calculation. Figure 6-8 shows the membership design function type-2 fuzzy logic using Matlab. Where each uses 11 membership functions. While the image forecasting results shown in graphs 8 and 9. Graph 8 is the result of load forecasting and graph 9 is the error of forecasting results with the method of comparison of type-1 fuzzy logic. The data used is the peak load data of Sulselrabar electricity system started in 2012-2015 by using Interval Type-1 Fuzzy Logic method and Interval Type-2 Fuzzy Logic (IT2FL) as a comparison. Then, the data is devoted to four days before and during 1 Ramadhan 2016. The test results by using the IT2 method as a proposed method for load forecasting showed excellent results, in which the Mean Absolute Percentage Error (MAPE) of VLDMAX is 1.344510913%. By using IT1FL, MAPE is 1.607778264%. For complete results can be seen in figure 9-10. LONTAR KOMPUTER VOL. 9, NO. 3 DECEMBER 2018 p-ISSN 2088-1541 DOI : 10.24843/LKJITI.2018.v09.i03.p04 e-ISSN 2541-5832 Accredited B by RISTEKDIKTI Decree No. 51/E/KPT/2017 155 Figure 9. Results of Load Forecast for 1 st Ramadhan in 2016 Figure 10. Results of Load Forecasting Error on 1 st Ramadhan in 2016 5. Conclusions Electrical Load Forecasting Day on the 1st of Ramadhan using intelligent methods based on Fuzzy Logic obtained very satisfactory results, with a very small error, this method is best used for short-term forecasting, medium and long-term. Error using Fuzzy Logic Type-2 of 1.607778264%, while using the proposed method Interval Type-2 Fuzzy Logic error is getting smaller in the amount of 1.344510913%. The application of intelligent methods for optimization of load forecasting is also highly recommended for yan forecasting methods used by PT. Perusahaan Listrik Negara (PLN) also still produce a sizable error. References [1] A. Srivastava, A. S. Pandey, and D. Singh, "Short-term load forecasting methods: A review," in Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES), International Conference on, 2016, pp. 130-138. [2] A. 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