Comparison of  Short-Term Load Forecasting Based on Kalimantan Data

Dinata, Syalam Ali Wira; Azka, Muhammad; Hasanah, Primadina; Suhartono, Suhartono; Gamal, M. D. H.

doi:10.29244/ijsa.v5i2p243-259

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Jika data mengalami pola musiman ganda berulang seperti hari dan tahun, maka dapat menggunakan metode Double SARIMA yang merupakan pengembangan dari metode SARIMA. Beberapa metode peramalan dengan tingkat keberhasilan yang bervariasi telah diimplementasikan untuk peramalan beban listrik termasuk pada penelitian Syalam, dkk [2][3][4]. Selanjutnya, terdapat juga pengembangan dari metode Double SARIMA yaitu Triple SARIMA yang berkaitan dengan data seasonal berulang seperti hari, bulan, dan tahun.…”

Section: Pendahuluanunclassified

Triple-Seasonal ARIMA Untuk Peramalan Data Konsumsi Beban Listrik

Azizah,

Nugraheni,

Dinata

2023

Contemp. Math. App

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Dalam kehidupan, listrik merupakan salah satu sumber energi yang penting dan utama untuk memenuhi kebutuhan hidup manusia, baik dalam bidang industri, ekonomi maupun teknologi. Seiring dengan meningkatnya kebutuhan listrik dari waktu ke waktu, permasalahan yang dihadapi adalah kuantitas daya yang disalurkan, sehingga penyaluran listrik ke konsumen harus dioptimalkan sesuai dengan kebutuhan. Tujuannya agar dapat mengambil tindakan yang tepat berdasarkan kebutuhan listrik dan meningkatkan kualitas pelayanan kepada konsumen. Oleh karena itu, diperlukan metode peramalan beban listrik yang efektif di masa mendatang untuk mengoptimalkan kebutuhan listrik. Pada penelitian ini dilakukan peramalan dengan metode Triple SARIMA dengan menggunakan data sampel negara Denmark selama 60 menit dalam satuan Mega Watt yang diperoleh dari data sekunder open source (https://data.open-power-system-data.org/time_series/ 2020-10-06). Peramalan ini dilakukan dengan memperhitungkan faktor musiman harian, mingguan, dan tahunan. Tujuan dari penelitian ini adalah untuk mengetahui pola data beban listrik triple-seasonal terhadap pengaruh waktu periodik, mengevaluasi model triple-seasonal terbaik untuk mendapatkan minimum error dan model musiman subset, multiplicative dan additive. Hasil yang diperoleh dalam penelitian ini diperoleh 3 model sementara, kemudian dilakukan uji estimasi dan uji signifikansi dengan uji asumsi Maximum likelihood dan residual.

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Section: Pendahuluanunclassified

Triple-Seasonal ARIMA Untuk Peramalan Data Konsumsi Beban Listrik

Azizah,

Nugraheni,

Dinata

2023

Contemp. Math. App

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“…Often in data processing, a time series data is encountered that has more than one seasonal pattern, such as short-term electricity consumption data. Triple Seasonal Autoregressive Integrated Moving Average or commonly referred to as TSARIMA which can be said if it has three seasonal patterns, in general from the development of the ARIMA, SARIMA, and DSARIMA models mathematically into the TSARIMA model can be written as follows [13]:…”

Section: Triple Seasonal Autoregressive Integrated Moving Average (Ts...mentioning

confidence: 99%

Comparison in Predicting the Short-Term Using the Sarima, Dsarima and Tsarima Methods

Giovani

Anggriani

Simatupang

2022

BAREKENG: J. Math. & App.

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The flow of data and information is growing rapidly and rapidly in various sizes and means which is called Big Data. In the face of a change for the better in the future, a careful analysis and design of a data processing system is needed, in which a predictive framework can formulate the right policy to be one of the efforts to make a good decision. This is one of the appropriate Big Data processing efforts, which can be realized through one of the methods, namely prediction or forecasting is an effort to predict future values or trends as a reference for analyzing conditions in the past. One example of Big Data in the City of Balikpapan, namely the temperature within 2 meters obtained from the NASA satellite published on the website power.larc.nasa.gov. One of the methods used in this research is the ARIMA method and it is developed according to the data used. Based on the data to be used, namely temperature data within a distance of 2 meters in the city of Balikpapan, the development of data processing is carried out to pay attention to three seasonal patterns or the so-called Triple Seasonal ARIMA model. In this study, it can be seen how to build the Triple Seasonal ARIMA model and comparison with alternative models, namely Seasonal ARIMA and Double Seasonal ARIMA, and can see how the results of the Triple Seasonal ARIMA model accuracy when compared with alternative models. The method used in this study is the Seasonal ARIMA, Double Seasonal ARIMA and Triple Seasonal ARIMA methods. The results obtained in this study obtained a comparison of methods in making predictions with a specified time span, the results obtained from the Seasonal ARIMA model that it was very good at predicting a time span of 2 weeks, Double Seasonal ARIMA for a period of 1 month, Double Seasonal ARIMA for a period of 3 months, and Triple Seasonal ARIMA for a period of 6 months.

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“…In Indonesia, STLF studies have been carried out on the electricity systems in Kalimantan [34], Java-Bali [35], [36], South Sulawesi, Southeast Sulawesi, and West Sulawesi [37], and Papua-Maluku [38]. In particular, STLF studies on the electricity system in South Sulawesi, Southeast Sulawesi, and West Sulawesi [37] have investigated the method of obtaining forecast input data, including weather data from weather observation locations representing load centers in the coverage area of the electricity system.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting using Artificial Neural Network in Indonesia

Sumarauw

2023

Ilk. J. Ilm.

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Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating costs increase and ultimately lead to waste. STLF research in Indonesia, especially the State Electricity Company (PLN Sulselrabar), has yet to be widely used. Methods mainly used are manual and conventional methods because they are considered adequate. In addition, Indonesia's geographical conditions are extensive and diverse, and the electricity system is complex. As a result, the factors affecting each country's electricity demand are different, so unique forecasting methods are needed. Artificial Neural Network (ANN) is one of the Artificial Intelligent (AI) methods widely used for STLF because it can model complex and non-linear relationships from networks. This paper aims to build an STLF forecasting model that is suitable for Indonesia's geographical conditions using several ANN models tested. Based on several ANN forecasting models, the test results obtained the best model is Model-6 with ANN architecture (9-20-1). This model has one hidden layer, 20 neurons in the hidden layer, a sigmoid logistic activation function (binary sigmoid), and a linear function. Forecasting performance values obtained mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 430.48 MW2, 15.07 MW, and 2.81%, respectively.

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Comparison of Short-Term Load Forecasting Based on Kalimantan Data

Cited by 3 publications

References 11 publications

Triple-Seasonal ARIMA Untuk Peramalan Data Konsumsi Beban Listrik

Triple-Seasonal ARIMA Untuk Peramalan Data Konsumsi Beban Listrik

Comparison in Predicting the Short-Term Using the Sarima, Dsarima and Tsarima Methods

Short-Term Load Forecasting using Artificial Neural Network in Indonesia

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