Forecasting Electricity Demand using Seasonal ARIMA and Asymmetric GARCH Models: A Case Study for Malaysia
DOI:
https://doi.org/10.22452/mjs.vol45sp2.1Keywords:
Forecasting Electricity Demand, ARIMA, GARCH, AsymmetricAbstract
Accurate energy load forecasting is important for enhancing functionality and reliability of strategy development within the power generation industry. Electricity systems exhibit distinct reactions to positive and negative demand shocks, making asymmetric models particularly useful for capturing these variations. Incorporating asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models into electricity demand forecasting can enhance forecasting accuracy by capturing the responses to demand shocks. It has been demonstrated that non-seasonal Autoregressive Integrated Moving Average (ARIMA) with asymmetric GARCH-type model is promising for forecasting highly volatile data. However, Malaysia's electricity demand time series data exhibits strong seasonal fluctuations, necessitating the use of seasonal models. From this perspective, an evaluation is carried out in this study to determine the effectiveness of a seasonal ARIMA model combined with an asymmetric GARCH-type model for forecasting electricity demand in Malaysia. Additionally, it proposes a new procedure for forecasting electricity demand. This study utilized weekly peak electricity usage over the timeframe of 2005 to 2016. The results suggest the optimal combination of seasonal ARIMA with asymmetric GARCH-type model for electricity demand prediction is integrated with structure using Student’s t-distribution. This model was selected due to its simplicity and strong performance, reflected in its low error metrics: root mean squared error (RMSE) of 1021.6340, mean absolute error (MAE) of 867.6399, and mean absolute percentage error (MAPE) of 5.14%. The study demonstrates the model’s superiority in capturing volatility and seasonality compared to traditional approaches. Given its enhanced predictive performance, the proposed seasonal ARIMA with asymmetric GARCH-type model provides critical insights for policymakers and energy sector planners in forecasting electricity demand in Malaysia.
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