Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia

Mado, Ismit; Soeprijanto, Adi; Suhartono, Suhartono

doi:10.11591/ijece.v8i6.pp4892-4901

Cited by 16 publications

(15 citation statements)

References 11 publications

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“…The ARIMA approach uses three parameters: p, d, and q. The ARIMA model's p parameter indicates the number of lag periods [23], [24]. For example, if p=2 is used in the auto-regression component of the equation, two preceding periods of the time series are employed.…”

Section: Arimamentioning

confidence: 99%

High performance time series models using auto autoregressive integrated moving average

Al-Qazzaz

Yousif

2022

IJEECS

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Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated mov ing average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of using Auto ARIMA over ARIMA for determining the appropriate ARIMA parameters based on measures such as root mean square error ( RMSE ) , mean absolute error ( MAE ) , and aka ike information criterion ( AIC ) without requiring the attention of an expert data scientist as it bypasses several steps needed for manual ARIMA. Auto ARIMA produced an RMSE of 12.5539 and an AIC of 1877.224, which are comparable to the values resulting fr om the manual ARIMA with the help of expert data scientists; thus, it saves analysis time and offers the best model result.

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Section: Arimamentioning

confidence: 99%

High performance time series models using auto autoregressive integrated moving average

Al-Qazzaz

Yousif

2022

IJEECS

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“…These models are classical forecasting models and they are widely used in in this field. The models are: the temporal NBLM [9], the Holt-Winters (HW) [26], Auto Regressive Integrated Moving Average (ARIMA) [14], and the Auto Regressive Moving Average (ARMA) method as in [7]. A single season for the HW, the ARMA and the ARIMA models is used because they are cyclic-based models and need a repeated pattern.…”

Section: Forecasting Accuracymentioning

confidence: 99%

“…Although several short term forecasting models have been proposed, overdispersion has not been appropriately handled in these models. The statistical forecasting models such as regression based models and smoothing based models assume that the variance is equal to the mean [4,7,13], or they utilize distributions that ignore the high variations [14,15]. In other models, the high variance in time series is treated to become homogeneous and stationary by Box-Cox and differentiating transformations which increase the computational demand [7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Daraghmi

Daadoo

et al. 2020

IJEECS

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<div>Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient</div><div>energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char</div><div>acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns.</div><div>Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting</div><div>methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel</div><div>model for short-term electric load forecasting. The model adopts the negative binomial additive models</div><div>(NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season</div><div>ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of</div><div>load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM</div><div>captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has</div><div>low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its</div><div>accuracy and effificiency outperform those of the other models used in this context.</div>

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“…The time series prediction model is an accurate choice and continues to grow to this day [5][6][7]. Researchers have carried out load forecasting study activities with 2.06 percent MAPE [8]. In research, the parameter estimation pattern was developed again with the least squares method which is better.…”

Section: Introductionmentioning

confidence: 99%

Electric Load Forecasting an Application of Cluster Models Based on Double Seasonal Pattern Time Series Analysis

Mado¹

2021

Forecasting in Mathematics - Recent Advances, New Perspectives and Applications

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Electricity consumption always changes according to need. This pattern deserves serious attention. Where the electric power generation must be balanced with the demand for electric power on the load side. It is necessary to predict and classify loads to maintain reliable power generation stability. This research proposes a method of forecasting electric loads with double seasonal patterns and classifies electric loads as a cluster group. Double seasonal pattern forecasting fits perfectly with fluctuating loads. Meanwhile, the load cluster pattern is intended to classify seasonal trends in a certain period. The first objective of this research is to propose DSARIMA to predict electric load. Furthermore, the results of the load prediction are used as electrical load clustering data through a descriptive analytical approach. The best model DSARIMA forecasting is ([1, 2, 5, 6, 7, 11, 16, 18, 35, 46], 1, [1, 3, 13, 21, 27, 46]) (1, 1, 1)48 (0, 0, 1)336 with a MAPE of 1.56 percent. The cluster pattern consists of four groups with a range of intervals between the minimum and maximum data values divided by the quartile. The presentation of this research data is based on data on the consumption of electricity loads every half hour at the Generating Unit, the National Electricity Company in Gresik City, Indonesia.

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Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia

Cited by 16 publications

References 11 publications

High performance time series models using auto autoregressive integrated moving average

High performance time series models using auto autoregressive integrated moving average

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Electric Load Forecasting an Application of Cluster Models Based on Double Seasonal Pattern Time Series Analysis

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