Double Seasonal ARIMA for Forecasting Electricity Demand of Kuaro Main Gate in East Kalimantan

Azka, Muhammad; Wiradinata, SA; Faisal, Muliady; Suhartono, Suhartono

doi:10.1088/1757-899x/846/1/012064

Cited by 3 publications

(2 citation statements)

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“…Double Seasonal Autoregressive Integrated Moving Average or commonly referred to as DSARIMA which can be said if it has two seasonal patterns, the DSARIMA model can be written as follows [4].…”

Section: Double Seasonal Autoregressive Integrated Moving Average (Ds...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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Section: Double Seasonal Autoregressive Integrated Moving Average (Ds...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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“…Seasonal Autoregressive Integrated Moving Average (SARIMA) models which is an extension of the Autoregressive Integrated Moving Average (ARIMA) models incorporating the seasonality feature, were considered by the studies of Kadilar et al (2009), Etuk (2012), Etuk (2013), Etuk (2014) and Al-Gounmeein & Ismail (2020) In summary, many authors in previous studies applied different statistical models to forecast exchange rates. The literature such as Mohamed et al (2010), Mado et al (2018) and Azka et al (2020) claimed that the Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) models were mainly applied in forecasting load demand and electrical power demand.…”

Section: Introductionmentioning

confidence: 99%

Forecasting exchange rates in Sri Lanka: a comparison of the double seasonal autoregressive integrated moving average models (DSARIMA) and SARIMA models

Basnayake

Chandrasekara

2022

J Sci.Univ.Kelaniya

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Exchange rates serve as a medium for currency trading in the financial market. The variations and the uncertainty movements in exchange rates have a potential effect on the performance of a country. The objective of this study is to forecast daily exchange rates in Sri Lanka using Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) with Autoregressive Conditional Heteroscedasticity (ARCH)/ Generalized ARCH (GARCH) models. The study collected a few daily exchange rates from the Yahoo finance website in terms of LKR from 1 st January 2008 to 28 th February 2022. The DSARIMA and SARIMA models were incorporated with the ARCH/ GARCH specifications of normal, skew-normal, student-t and skew-t due to the accurate specification of the proper error distribution led to an increase in the accuracy of the fitted model. The model comparisons were carried out considering different performance measures.The overall results from the actual and fitted graphs and lower error values of the fitted models suggested a SARIMA model for CHF/LKR, a SARIMA model with ARCH/GARCH for USD, EURO, JPY, GBP and AUD against LKR and a DSARIMA model with ARCH/GARCH for CAD and SGD against LKR were suitable to forecast the respective exchange rate. Overall, the results from this study will support government, investors, corporate, financial and managerial sectors in their future decisions to accomplish their objectives. The originality of this study concerns the application of DSARIMA models in exchange rates due to the availability of double seasonality in data.

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Forecasting electricity loads on national holidays in East Kalimantan

Hasanah

Wiradinata

Azka

et al. 2021

J. Phys.: Conf. Ser.

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In the last decades, electricity becomes essential problems in East Kalimantan. Some previous research has conducted to assess the power plant systems and to examine the future electricity demand during the period. Moreover, there are some particular events such as national holidays that bring different impacts on the electricity demand. Forecasting electricity loads on national holidays will give significant information to the stakeholders in order to prevent the power outage of electricity. Two samples of hourly electricity loads in national holidays; New Year and Independence Day during 2015-2018 is analyzed in this research. Furthermore, the SARIMA model is used to estimate the suitable model to predict the future electricity loads. The result shows that SARIMA (2,2,0)(0,1,0)24 and SARIMA (1,1,0)(1,1,0)24 are the appropriate model for New Year and Independence Day, respectively. In addition, it is predicted that in 2020 the electricity demand on New Year will rise to 400 MegaWatt based on 95% of confidence intervals. On Independence Day, the electricity consumption will reach 300-450 MegaWatt in 2019 and 2020 with significant growth up to 10% each year.

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Double Seasonal ARIMA for Forecasting Electricity Demand of Kuaro Main Gate in East Kalimantan

Cited by 3 publications

References 0 publications

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

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

Forecasting exchange rates in Sri Lanka: a comparison of the double seasonal autoregressive integrated moving average models (DSARIMA) and SARIMA models

Forecasting electricity loads on national holidays in East Kalimantan

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