Implementation of Fuzzy Time Series Markov Chain Method to Predict Electricity Consumption in Aceh Province

Virza Gavinda, Nurdin Nurdin, Fajriana Fajriana

Abstract


The use of electrical energy is expected to always increase every year. This is due to the increasing needs of the community that must be met. Electricity has become the foundation for welfare and economic progress as well as a growth engine both domestically and globally, to meet the need for electrical energy in the future so that a system is needed that can predict future electrical energy consumption. Various prediction methods have developed along with the problems that arise. These methods include the Fuzzy Time Series Markov Chain method and the Markov Chain Monte Carlo method. This study aims to apply the Fuzzy Time Series Markov Chain (FTSMC) method to predict electricity consumption (KWH) in Aceh Province until 2030 using the electricity consumption dataset from 2018 to 2022. The FTSMC method combines fuzzy time series modeling with Markov chain state transitions, allowing for effective handling of uncertainty in time series data. The results reveal an impressive forecast accuracy, with a Mean Absolute Percentage Error (MAPE) of 3.2483%, demonstrating the model’s robustness and suitability for electricity consumption forecasting, with a predicted 314,606,308 kWh in January 2023 and 482,982,495 kWh in December 2030, representing an overall increase of 53.5% over the eight-year period. The FTSMC model effectively stabilizes predictions over time, ultimately converging to a stable value. This stability suggests that FTSMC is well-suited for forecasting in contexts where historical patterns are expected to persist. Further application of this model could benefit other sectors requiring accurate, stable forecasts.

Keywords


Fuzzy Time Series; Markov Chain; Electricity Consumption; Forecasting; MAPE

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DOI: https://doi.org/10.32520/stmsi.v14i5.4858

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