Demand forecasting of electricity in Indonesia with limited historical data

Kartikasari, Mujiati Dwi; Prayogi, Arif Rohmad

doi:10.1088/1742-6596/974/1/012040

Cited by 12 publications

(6 citation statements)

References 3 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%

“…Based on Table 1, the data has met the white noise criteria with a p-value greater than the error tolerance value 𝛼 = 5%, with an alpha significance level of less than 0.0001. In addition, the model at the time of initial estimation has an improvement pattern with 3 MA parameters, namely MA(1,1), MA(2,1) and MA (3,1), so these three parameters must be included in the model estimation. While the residual assumption test which includes the assumption of white noise must meet the independent criteria and have a normal distribution (0, 𝜎 $ ).…”

Section: B Parameter Estimationmentioning

confidence: 99%

“…Forecasting electrical power demand is an important step in the planning and operation of the generating system [1]. Forecasting results are used to prepare the planning and operation of the generating system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: B Parameter Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Electricity Load Forecasting Model Based DSARIMA

Mado

Rajagukguk

Triwiyatno

et al. 2022

IJEEPSE

View full text Add to dashboard Cite

show abstract

“…The results show that load forecasting methodology base on land use simulation has a smaller error (0.032%) than trending methods (0.126%) or Gompertz analysis (4.571%). Generally, the error tolerance level statistically has prediction under 10% (Kartikasari & Prayogi, 2018). Thus, it can be inferred that the prediction model obtained is sufficient.…”

Section: Methodology Comparison Testingmentioning

confidence: 97%

Development of Micro-Spatial Electricity Load Forecasting Methodology Using Multivariate Analysis for Dynamic Area in Tangerang, Indonesia

Senen¹,

Widyastuti²,

Handayani³

et al. 2021

JST

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Dynamic population and land use significantly affect future energy demand. This paper proposes a suitable method to forecast load growth in a dynamic area in Tangerang, Indonesia. This research developed micro-spatial load forecasting, which can show load centres in microgrids, estimate the capacity and locate the distribution station precisely. Homogenous grouping implemented the method into clusters consisted of microgrids. It involves multivariate variables containing 12 electric and non-electric variables. Multivariate analysis is conducted by carrying out Principal Component Analysis (PCA) and Factor Analysis. The forecasting results can predict load growth, time, and location, which can later be implemented as the basis of a master electricity distribution plan because it provides an accurate long-term forecast.

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“…Generally, the moving average method use to statistical prediction in business and engineering purposes. Some applications of moving average for forecasting are predicting stock price [11], counting electricity demands [12], and foreseeing total delivery goods [13].…”

Section: Related Studiesmentioning

confidence: 99%

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Yudianto¹,

Agustin²,

James³

et al. 2021

IJMECS

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Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

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Demand forecasting of electricity in Indonesia with limited historical data

Cited by 12 publications

References 3 publications

Short-Term Electricity Load Forecasting Model Based DSARIMA

Short-Term Electricity Load Forecasting Model Based DSARIMA

Development of Micro-Spatial Electricity Load Forecasting Methodology Using Multivariate Analysis for Dynamic Area in Tangerang, Indonesia

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

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