Studi Perbandingan Peramalan Intensitas Radiasi Matahari di Kota Malang Menggunakan Metode ANFIS dan Regresi Linier Berganda

Perkembangan penggunaan pembangkit listrik tenaga surya semakin lama semakin meningkat. Energi listrik yang dihasilkan memanfaatkan energi yang diterima dari sinar matahari. Besaran intensitas matahari bergantung oleh kondisi cuaca dan iklim lingkungan sekitar. Keadaan cuaca dari waktu ke waktu selalu berubah dan terkadang tidak menentu. Namun demikian keadaan cuaca ini dapat diprediksi dengan metode peramalan yang ada. Banyak metode yang dapat digunakan meramalkan keadaan cuaca baik berbasis matematika ataupun berbasis artificial intelligence. Untuk mengakomodir kedua metode tersebut pada penelitian ini metode regresi linier berganda dan Adaptive Neuro Fuzzy Inference System (ANFIS) digunakan untuk memprediksi intensitas ketersediaan energi matahari. Penelitian ini, bertujuan: 1). Mengetahui arsitektur ANFIS yang optimal untuk melakukan peramalan intensitas radiasi matahari; 2) Mengetahui peramalan intensitas radiasi matahari yang ada di lingkup Kota Malang dan Kota Bassel Swiss dengan metode Regresi Linier Berganda; 3) Mengetahui peramalan intensitas radiasi matahari yang ada di Kota Malang dan Kota Bassel Swiss dengan metode ANFIS; 4) Mengetahui perbandingan ramalan intensitas radiasi matahari antara metode Regresi Linier Berganda dan ANFIS; 5) Mengatahui perbandingan ramalan short term di Kota Malang dan Bassel menggunakan metode ANFIS dan Regresi Linier Berganda. Hasil penelitian menunjukan: 1) Arsitektur ANFIS yang optimal untuk melakukan peramalan intensitas radiasi matahari adalah 5 membership function, kurva membership function Gaussian Combination, dan 90% data training 10% data testting; 2) Hasil peramalan intensitas radiasi matahari dengan metode Regresi Linier Berganda yang ada di Kota Malang memiliki nilai RMSE sebesar 107,4813 dan nilai MAE sebesar 86,7716 sedangkan di Kota Bassel memiliki nilai RMSE sebesar 101,9780 dan nilai MAE sebesar 71,0880; 3) Hasil peramalan intensitas radiasi matahari dengan metode ANFIS yang ada di Kota Malang memiliki nilai RMSE sebesar 128,665 dan nilai MAE sebesar 101,531 sedangkan di Kota Bassel memiliki nilai RMSE sebesar 99,2813 dan nilai MAE sebesar 71,9695; 4) Perbandingan peramalan untuk jangka waktu yang lama dengan data Kota Bassel. Melihat dari nilai MAE dan RMSE dapat diketahui metode ANFIS lebih akurat dalam melakukan peramalan intensitas radiasi matahari, sedangkan Regresi Linier Berganda baik dalam melakukan peramalan intensitas radiasi matahari dengan data yang sedikit; 5) Perbandingan peramalan short term di Kota Malang dengan metode ANFIS memiliki nilai RMSE 114,0588 MAE 96,3178 dan metode Regresi Linier Berganda memiliki nilai RMSE 95,6449 MAE 80,7259 sedangkan peramalan short term di Kota Bassel dengan metode ANFIS memiliki nilai RMSE 81,2167 MAE 63,66577 dan metode Regresi Linier Berganda memiliki nilai RMSE 108,9498 MAE 70,1278

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method

STUDI PERBANDINGAN PERAMALAN INTENSITAS RADIASI MATAHARI DI KOTA MALANG MENGGUNAKAN METODE ANFIS DAN REGRESI LINIER BERGANDA

Perkembangan penggunaan pembangkit listrik tenaga surya semakin lama semakin pesat. Energi listrik yang dihasilkan sendiri memanfaatkan enregi yang diterima dari sinar matahari. Cuaca yang tidak menentu sendiri ini berubah seiiring waktu tetapi cuaca sendiri dapat diprediksi dengan metode-metode yang ada saat ini. Metode peramalan menggunakan metode artificial intelligence yaitu Adaptive Neuro Fuzzy Inference (ANFIS) dan menggunakan metode konvensional berup metode Regresi Berganda sebagai pembanding. Hasil penelitian menunjukan: 1) Arsitektur ANFIS yang optimal untuk peramalan  adalah 5 mf, kurva mf Gaussian Combination, dan pembagian data 90%-10%; 2) Hasil peramalan dengan metode Regresi Berganda di Kota Malang memiliki nilai RMSE 107,4813 MAE 86,7716 sedangkan di Kota Bassel memiliki nilai RMSE  101,9780 dan MAE 71,0880; 3) Hasil peramalan dengan ANFIS di Kota Malang memiliki nilai RMSE 128,665 dan nilai MAE 101,531 sedangkan di Kota Bassel memiliki nilai RMSE 99,2813 nilai MAE 71,9695; 4) Perbandingan peramalan  jangka waktu yang lama data Kota Bassel. Melihat dari nilai error dapat diketahui metode ANFIS lebih akurat dalam melakukan peramalan intensitas radiasi matahari; 5) Perbandingan peramalan short term di Kota Malang dengan metode ANFIS memiliki nilai RMSE 114,0588 MAE 96,3178 dan Regresi Berganda memiliki nilai RMSE 95,6449 MAE 80,7259 sedangkan peramalan di Kota Bassel dengan metode ANFIS memiliki nilai RMSE 81,2167 MAE 63,66577  dan metode Regresi Berganda memiliki nilai RMSE 108,9498 MAE 70,1278; Kata kunci: Peramalan, ANFIS, Regresi Berganda, MAE, RMSE, Solar Radiation ABSTRACT The development of the use of solar power plants increasingly rapidly. The self-generated electric energy utilizes the energies received from the sun. The erratic weather itself changes over time but the weather itself can be predicted by the methods that exist today. Forecasting method using artificial intelligence method that is Adaptive Neuro Fuzzy Inference (ANFIS) and using conventional method to multiply Regression method as comparison. The results showed: 1) The optimal ANFIS architecture for forecasting was 5 mf, Gaussian Combination mf curve, and 90% -10% clustering data; 2) Forecasting result with Multiple Regression method in Malang has RMSE 107,4813 MAE 86,7716 whereas in Bassel City has value RMSE 101,9780 and MAE 71,0880; 3) Forecasting result with ANFIS in Malang has RMSE 128,665 and MAE value 101,531 whereas in  Bassel City has RMSE 99,2813 MAE value 71,9695; 4) Comparison of long-time data forecasting Bassel City. Viewing from the error value can be known ANFIS method is more accurate in forecasting the intensity of solar radiation; 5) Comparison of short term forecasting in Malang with ANFIS method has RMSE 114,0588 MAE 96,3178 and Multiple Regression has RMSE value 95,6449 MAE 80,7259 while forecasting in Bassel City with ANFIS method has RMSE 81,2167 MAE 63,66577 and the method of Multiple Regression has RMSE value 108,9498 MAE 70,1278; Keywords: forecasting, ANFIS, Multiple Regresion, MAE, RMSE, Solar Radiatio

STUDI PERBANDINGAN PERAMALAN INTENSITAS RADIASI MATAHARI DI KOTA MALANG MENGGUNAKAN METODE ANFIS DAN REGRESI LINIER BERGANDA

Perkembangan penggunaan pembangkit listrik tenaga surya semakin lama semakin pesat. Energi listrik yang dihasilkan sendiri memanfaatkan enregi yang diterima dari sinar matahari. Cuaca yang tidak menentu sendiri ini berubah seiiring waktu tetapi cuaca sendiri dapat diprediksi dengan metode-metode yang ada saat ini. Metode peramalan menggunakan metode artificial intelligence yaitu Adaptive Neuro Fuzzy Inference (ANFIS) dan menggunakan metode konvensional berup metode Regresi Berganda sebagai pembanding. Hasil penelitian menunjukan: 1) Arsitektur ANFIS yang optimal untuk peramalan  adalah 5 mf, kurva mf Gaussian Combination, dan pembagian data 90%-10%; 2) Hasil peramalan dengan metode Regresi Berganda di Kota Malang memiliki nilai RMSE 107,4813 MAE 86,7716 sedangkan di Kota Bassel memiliki nilai RMSE  101,9780 dan MAE 71,0880; 3) Hasil peramalan dengan ANFIS di Kota Malang memiliki nilai RMSE 128,665 dan nilai MAE 101,531 sedangkan di Kota Bassel memiliki nilai RMSE 99,2813 nilai MAE 71,9695; 4) Perbandingan peramalan  jangka waktu yang lama data Kota Bassel. Melihat dari nilai error dapat diketahui metode ANFIS lebih akurat dalam melakukan peramalan intensitas radiasi matahari; 5) Perbandingan peramalan short term di Kota Malang dengan metode ANFIS memiliki nilai RMSE 114,0588 MAE 96,3178 dan Regresi Berganda memiliki nilai RMSE 95,6449 MAE 80,7259 sedangkan peramalan di Kota Bassel dengan metode ANFIS memiliki nilai RMSE 81,2167 MAE 63,66577  dan metode Regresi Berganda memiliki nilai RMSE 108,9498 MAE 70,1278; Kata kunci: Peramalan, ANFIS, Regresi Berganda, MAE, RMSE, Solar Radiation ABSTRACT The development of the use of solar power plants increasingly rapidly. The self-generated electric energy utilizes the energies received from the sun. The erratic weather itself changes over time but the weather itself can be predicted by the methods that exist today. Forecasting method using artificial intelligence method that is Adaptive Neuro Fuzzy Inference (ANFIS) and using conventional method to multiply Regression method as comparison. The results showed: 1) The optimal ANFIS architecture for forecasting was 5 mf, Gaussian Combination mf curve, and 90% -10% clustering data; 2) Forecasting result with Multiple Regression method in Malang has RMSE 107,4813 MAE 86,7716 whereas in Bassel City has value RMSE 101,9780 and MAE 71,0880; 3) Forecasting result with ANFIS in Malang has RMSE 128,665 and MAE value 101,531 whereas in  Bassel City has RMSE 99,2813 MAE value 71,9695; 4) Comparison of long-time data forecasting Bassel City. Viewing from the error value can be known ANFIS method is more accurate in forecasting the intensity of solar radiation; 5) Comparison of short term forecasting in Malang with ANFIS method has RMSE 114,0588 MAE 96,3178 and Multiple Regression has RMSE value 95,6449 MAE 80,7259 while forecasting in Bassel City with ANFIS method has RMSE 81,2167 MAE 63,66577 and the method of Multiple Regression has RMSE value 108,9498 MAE 70,1278; Keywords: forecasting, ANFIS, Multiple Regresion, MAE, RMSE, Solar Radiatio

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method