Abstract
Electricity price forecasting is a good way of estimating the future electricity prices to a large extent in safe and peaceful conditions. Knowing an acceptable range of commodity prices in the future can positively reduce investment risk (for both producers and customers) and give more stability to the trading market. In this paper, the electricity price is forecasted using the autoregressive moving average method and the Generalized Autoregressive Conditional Heteroscedasticity model. These methods have been developed and applied to the Australian electricity market data, and in some case studies. The proposed learning model is capable of modeling the variance error in the highly correlated market data as well as dealing with the price of the electricity in real-time. The results are compared with the actual data that is collected with Internet of Things approach and show the convincing evidence of the model's validity and accuracy.
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