Author(s)

Intan Azmira Wan Abdul Razak^1*, Mohamad Fani Sulaima¹, Farah Anishah Zaini¹, Siti Noor Hidayah Abdul Rahman², Wan Syakirah Wan Abdullah³

¹Department of Electrical Engineering, Faculty of Electrical Engineering and Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia.
²Vencast Sdn. Bhd., Jalan Spring 34/23, Off Jalan Bukit Kemuning, Seksyen, Shah Alam, Malaysia.
³Department of Business Assessment and Engineering 2, TNB Renewables Sdn. Bhd., Jalan Bukit Pantai, Kuala Lumpur, Malaysia.

Accurate forecasting of the system marginal price (SMP) is crucial to improve demand-side management and optimize power generation scheduling. However, predicting the SMP is challenging due to the high volatility of electricity prices, which are influenced by fuel prices, fluctuations in energy demand, and generation capacity. Hence, the aim of this study is to improve the accuracy of SMP forecasting for the Malaysian electricity market using time-series models. Correlation analysis was first conducted to identify the most significant variables affecting SMP, followed by the development of simple linear regression (SLR), multiple linear regression (MLR), and autoregressive integrated moving average (ARIMA) models to predict the SMP, and assessment of the models using actual market data from a Single Buyer (SB) website. The accuracy of the SLR, MLR, and ARIMA models was assessed using mean absolute error (MAE) and mean absolute percentage error (MAPE). The prediction accuracy of the models was significantly improved by tailoring the model parameters and selecting the relevant independent variables. Based on the results, the SLR, MLR, and ARIMA models outperformed other models published in the literature including the model used by the SB. The regression and ARIMA models appear promising for forecasting electricity prices accurately, offering valuable insights to power utilities and stakeholders in supporting informed decision-making in a dynamic electricity market.

ARIMA, Correlation Analysis, Linear Regression, System Marginal Price Forecasting

Razak, I. , Sulaima, M. , Zaini, F. , Rahman, S. and Abdullah, W. (2025) Forecasting the System Marginal Price in the Malaysian Electricity Market Using Time-Series Models. Journal of Power and Energy Engineering, 13, 61-80. doi: 10.4236/jpee.2025.138005.