Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA)

Rinaldi Rusli; Achmad Nizar Hidayanto; Yova Ruldeviyani

doi:10.1109/IWBIS.2019.8935844

Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA)

Rinaldi Rusli, Achmad Nizar Hidayanto, Yova Ruldeviyani

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

PT. PLN (Persero) is Indonesia state-owned company engaged in the electricity sector. In serving the needs of electricity in Indonesia, about 49.91% power plant is a Coal Fired Steam Power Plant (PLTU). To maintain coal operations, PLN has an rregulation to maintain the average coal stock limit of 22-26 days of operation; in addition PLN has also conduct a Coal Online Application (BBO) which records coal stock conditions in each power plant. Even though PLN have already create a regulations and online applications to monitor the power plants, coal stocks in several power plants are still experiencing a stock crisis caused by volatility in coal power plant needs due to changing capacity factor (CF) and specific fuel consumption (SFC). Therefore PLN need a model to estimate coal demand at the power plant so that it can help mmanagement make decisions early before the stock crisis.This study predicts coal consumption from time series CF data, SFC data; coal received and coal consumption taken from BBO applications for the period 2013 to 2017. The algorithm used is a hybrid between the Autoregressive Integrated Moving Average (ARIMA) model and the Particle Swarm Optimization (PSO). The results of applying data mining to predict coal needs at the coal-fired steam power plant Indramayu using ARIMA compared to hybrid ARIMA and PSO, has been proven increasing its accuracy by decreasing Mean Absolute Percentage Error (MAPE) from 9.03% to 4.69%.

Original language	English
Title of host publication	2019 International Workshop on Big Data and Information Security, IWBIS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	15-20
Number of pages	6
ISBN (Electronic)	9781728153476
DOIs	https://doi.org/10.1109/IWBIS.2019.8935844
Publication status	Published - Oct 2019
Event	2019 International Workshop on Big Data and Information Security, IWBIS 2019 - Bali, Indonesia Duration: 11 Oct 2019 → …

Publication series

Name	2019 International Workshop on Big Data and Information Security, IWBIS 2019

Conference

Conference	2019 International Workshop on Big Data and Information Security, IWBIS 2019
Country/Territory	Indonesia
City	Bali
Period	11/10/19 → …

Keywords

ARIMA
coal consumption prediction
coal-fired steam power plant (PLTU)
PLN
PSO
state-owned company

Access to Document

10.1109/IWBIS.2019.8935844

Cite this

Rusli, R., Hidayanto, A. N., & Ruldeviyani, Y. (2019). Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). In 2019 International Workshop on Big Data and Information Security, IWBIS 2019 (pp. 15-20). Article 8935844 (2019 International Workshop on Big Data and Information Security, IWBIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS.2019.8935844

Rusli, Rinaldi ; Hidayanto, Achmad Nizar ; Ruldeviyani, Yova. / Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). 2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 15-20 (2019 International Workshop on Big Data and Information Security, IWBIS 2019).

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title = "Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA)",

abstract = "PT. PLN (Persero) is Indonesia state-owned company engaged in the electricity sector. In serving the needs of electricity in Indonesia, about 49.91% power plant is a Coal Fired Steam Power Plant (PLTU). To maintain coal operations, PLN has an rregulation to maintain the average coal stock limit of 22-26 days of operation; in addition PLN has also conduct a Coal Online Application (BBO) which records coal stock conditions in each power plant. Even though PLN have already create a regulations and online applications to monitor the power plants, coal stocks in several power plants are still experiencing a stock crisis caused by volatility in coal power plant needs due to changing capacity factor (CF) and specific fuel consumption (SFC). Therefore PLN need a model to estimate coal demand at the power plant so that it can help mmanagement make decisions early before the stock crisis.This study predicts coal consumption from time series CF data, SFC data; coal received and coal consumption taken from BBO applications for the period 2013 to 2017. The algorithm used is a hybrid between the Autoregressive Integrated Moving Average (ARIMA) model and the Particle Swarm Optimization (PSO). The results of applying data mining to predict coal needs at the coal-fired steam power plant Indramayu using ARIMA compared to hybrid ARIMA and PSO, has been proven increasing its accuracy by decreasing Mean Absolute Percentage Error (MAPE) from 9.03% to 4.69%.",

keywords = "ARIMA, coal consumption prediction, coal-fired steam power plant (PLTU), PLN, PSO, state-owned company",

author = "Rinaldi Rusli and Hidayanto, {Achmad Nizar} and Yova Ruldeviyani",

year = "2019",

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Rusli, R, Hidayanto, AN & Ruldeviyani, Y 2019, Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). in 2019 International Workshop on Big Data and Information Security, IWBIS 2019., 8935844, 2019 International Workshop on Big Data and Information Security, IWBIS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 15-20, 2019 International Workshop on Big Data and Information Security, IWBIS 2019, Bali, Indonesia, 11/10/19. https://doi.org/10.1109/IWBIS.2019.8935844

Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). / Rusli, Rinaldi; Hidayanto, Achmad Nizar ; Ruldeviyani, Yova.
2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 15-20 8935844 (2019 International Workshop on Big Data and Information Security, IWBIS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rusli, Rinaldi

AU - Hidayanto, Achmad Nizar

AU - Ruldeviyani, Yova

PY - 2019/10

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N2 - PT. PLN (Persero) is Indonesia state-owned company engaged in the electricity sector. In serving the needs of electricity in Indonesia, about 49.91% power plant is a Coal Fired Steam Power Plant (PLTU). To maintain coal operations, PLN has an rregulation to maintain the average coal stock limit of 22-26 days of operation; in addition PLN has also conduct a Coal Online Application (BBO) which records coal stock conditions in each power plant. Even though PLN have already create a regulations and online applications to monitor the power plants, coal stocks in several power plants are still experiencing a stock crisis caused by volatility in coal power plant needs due to changing capacity factor (CF) and specific fuel consumption (SFC). Therefore PLN need a model to estimate coal demand at the power plant so that it can help mmanagement make decisions early before the stock crisis.This study predicts coal consumption from time series CF data, SFC data; coal received and coal consumption taken from BBO applications for the period 2013 to 2017. The algorithm used is a hybrid between the Autoregressive Integrated Moving Average (ARIMA) model and the Particle Swarm Optimization (PSO). The results of applying data mining to predict coal needs at the coal-fired steam power plant Indramayu using ARIMA compared to hybrid ARIMA and PSO, has been proven increasing its accuracy by decreasing Mean Absolute Percentage Error (MAPE) from 9.03% to 4.69%.

AB - PT. PLN (Persero) is Indonesia state-owned company engaged in the electricity sector. In serving the needs of electricity in Indonesia, about 49.91% power plant is a Coal Fired Steam Power Plant (PLTU). To maintain coal operations, PLN has an rregulation to maintain the average coal stock limit of 22-26 days of operation; in addition PLN has also conduct a Coal Online Application (BBO) which records coal stock conditions in each power plant. Even though PLN have already create a regulations and online applications to monitor the power plants, coal stocks in several power plants are still experiencing a stock crisis caused by volatility in coal power plant needs due to changing capacity factor (CF) and specific fuel consumption (SFC). Therefore PLN need a model to estimate coal demand at the power plant so that it can help mmanagement make decisions early before the stock crisis.This study predicts coal consumption from time series CF data, SFC data; coal received and coal consumption taken from BBO applications for the period 2013 to 2017. The algorithm used is a hybrid between the Autoregressive Integrated Moving Average (ARIMA) model and the Particle Swarm Optimization (PSO). The results of applying data mining to predict coal needs at the coal-fired steam power plant Indramayu using ARIMA compared to hybrid ARIMA and PSO, has been proven increasing its accuracy by decreasing Mean Absolute Percentage Error (MAPE) from 9.03% to 4.69%.

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KW - coal consumption prediction

KW - coal-fired steam power plant (PLTU)

KW - PLN

KW - PSO

KW - state-owned company

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Rusli R, Hidayanto AN , Ruldeviyani Y. Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). In 2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 15-20. 8935844. (2019 International Workshop on Big Data and Information Security, IWBIS 2019). doi: 10.1109/IWBIS.2019.8935844

Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA)

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