Short-Term load forecasting double seasonal ARIMA methods: An evaluation based on Mahakam-East Kalimantan data

Dinata, Syalam Ali Wira; Azka, Muhammad; Faisal, Muliady; Suhartono, Suhartono; Yendra, Rado; Gamal, M. D. H.

doi:10.1063/5.0017643

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…Based on the final parameter estimation results in Table 4, the ARIMA coefficient parameters are obtained as follows: AR ( Based on the prediction model parameters, the DSARIMA model ( [1,2,5,6,7,11,16,18,35,46], 1, [1,3,13,21,27,46]) , (1,1,1) 23 (0,0,1) 445 with the following model equation:…”

Section: Results Of Short Term Load Predictionmentioning

confidence: 99%

“…(1 − 1.1464𝐵 + 0.295𝐵 $ + 0.0104𝐵 9 − 0.0189𝐵 5 + 0.0234𝐵 : + 0.004𝐵 "" +0.0083𝐵 "5 + 0.0125𝐵 "3 + 0.0074𝐵 49 − 0.07𝐵 25 )(1 − 0.03𝐵 23 )𝑍 ! * = (1 − 0.934𝐵 + 0.077𝐵 4 − 0.008𝐵 "4 − 0.00685𝐵 $" − 0.017𝐵 $: −0.059𝐵 25 )(1 − 0.98𝐵 23 )(1 + 0.0364𝐵 445 )𝑎 !…”

Section: Results Of Short Term Load Predictionunclassified

“…Time series analysis has become a research trend, where the results of this study are very effective with a very high level of accuracy in predicting future electrical loads. Forecasting short-term electrical load based on time series has been carried out by several Indonesian researchers [3]- [5]. This time series forecasting method is then also compared with artificial intelligence methods, as has been done previously [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Electricity Load Forecasting Model Based DSARIMA

Mado

Rajagukguk

Triwiyatno

et al. 2022

IJEEPSE

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Forecasting short-term electrical load is very important so that the quality of the electrical power supplied can be maintained properly. The study was conducted to measure the results of electrical load forecasting based on parameter estimates and the presentation of time series data. It is important to manage stationary data, both in terms of mean and variance. Data presentation is done by determining the value of variance through the Box-Cox transformation method and the mean value based on the ACF and PACF plots. This study considers the pattern of electricity consumption which contains double seasonal patterns. The results of previous studies show the electric power prediction model, the DSARIMA model with a MAPE of 2.06%. The condition of the model used to predict the electrical load still has a tendency not to be normally distributed and it is estimated that there are outliers. Improvements to the AR and MA parameters that meet the standard error tolerance value of 5 percent are increased in this study. The results showed improvement of parameters to predict electrical load with DSARIMA model. The significance of this study was obtained by the MAPE value of 1.56 percent when compared to the actual data.

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Section: Results Of Short Term Load Predictionmentioning

confidence: 99%

Section: Results Of Short Term Load Predictionunclassified

Section: Introductionmentioning

confidence: 99%

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Short-Term Electricity Load Forecasting Model Based DSARIMA

Mado

Rajagukguk

Triwiyatno

et al. 2022

IJEEPSE

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“…Researchers applied least square estimation (LSE) based autoregressive integrated moving average (ARIMA) model for many a forecasting tasks [55], [56]. Seasonal ARIMA [57], [58] and double SARIMA [59]- [61] are powerful linear models extensively used to deal with various forecasting issues.…”

Section: A Literature Reviewmentioning

confidence: 99%

Short-Term Load Foresting Using Combination of Linear and Non-Linear Models

Rani,

Aggarwal,

Kumar

2024

IEEE Access

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Numerous short-term load forecasting models are available in the literature. However, the improvement in forecast accuracy using the combination models has yet to be analyzed on a daily rolling basis for a very long test period. In this paper, the characteristics of a combination of the Seasonal Autoregressive Integrated Moving Average (SARIMA) -a linear model and Radial Basis Function networks (RBFN) -a non-linear model have been studied in two different modeling frameworks namely single series (SS) and variable segmented series (VSS). The hourly load data of the Ontario Electricity Market (OEM) and the Iberian Electricity Market (MIBEL) are used as the case system to produce the daily load forecast. The impact on prediction accuracy by the size of training data and the combining individual forecasts has been studied for 12 years of OEM and one year of MIBEL demands, respectively. In order to achieve the empirical objective, a large number of models(1,447,740 in number) are estimated to produce load forecasts on a daily rolling basis. The forecast performance has been compared with the other models proposed in the literature.Among the linear models, for all window sizes of training data, the forecast accuracy of the combination model is better than the model selected with the minimum Akaike information criterion (AIC) and Bayesian information criterion (BIC) in both frameworks. Moreover, the ensemble of RBFN and linear models produces the best forecast. The results pinpointed that the proposed model's precision and stability are higher than the earlier forecasting models proposed for both markets. The novelty in the model is that only a single hourly time-series is used for forecasting and there is no need for other explanatory variables.

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“…The Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) is an extension of the ARIMA model that incorporates two seasonal periods. Several studies on the DSARIMA model, especially on electrical load data, have been conducted (Mohamed, et al, 2010 andDinata et al, 2020). The findings of the investigation indicate that the double seasonal ARIMA model is a good model for forecasting (Mohamed, et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting

Silfiani

Hayati

Azka

2023

STATKOM

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Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns. Objective: This study aims to identify the most accurate model for stock forecasting. Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202. Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy. Results: Following conducting each model, we assessed that the best models are DSARIMAX (0,1,[4]) ([3],1,1)5(1,1,0)253, regarding MASE and MdAPE corresponding to approximately 1.423 and 0.111. The scope of this study has limitations to a test set for one-month forecast periods. Conclusion: As stock prices rise, investors require precise forecasts. Models of forecasting must perform well. This analysis shows how the DSARIMA generate forecasts stock prices more accurately. This investigation evaluated the closing stock price of UNVR. Both MASE and MdAPE assess prediction. After analyzing each model, DSARIMAX (0,1,[4])([3],1,1)5(1,1,0)253 has the lowest MASE and MdAPE values, 1.423 and 0.111, respectively. The procedure lasted one month. Research may combine forecasts and improve their accuracy.

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Short-Term load forecasting double seasonal ARIMA methods: An evaluation based on Mahakam-East Kalimantan data

Cited by 5 publications

References 18 publications

Short-Term Electricity Load Forecasting Model Based DSARIMA

Short-Term Electricity Load Forecasting Model Based DSARIMA

Short-Term Load Foresting Using Combination of Linear and Non-Linear Models

Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting

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