Analysing and Forecasting Electricity Demand and Price Using Deep Learning Model during the COVID-19 Pandemic

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Conference Proceeding
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The smart city integrating the smart grid as an integral part of it to guaran-tee the ever-increasing electricity demand. After the recent outbreak of the COVID-19 pandemic, the socioeconomic severances affecting total levels of electricity demand, price, and usage trends. These unanticipated changes in-troducing new uncertainties in short-term demand forecasting since its result depends on the recent usage as an input variable. Addressing this challeng-ing situation, this paper proposes an electricity demand and price forecast model based on the LSTM Deep Learning method considering the recent demand trends. Real electricity market data from the Australian Energy Market Operator (AEMO) is used to validate the effectiveness of the pro-posed model and elaborated with two scenarios to get a wider context of the pandemic impact. Exploratory data analyses results show hourly electricity demand and price reductions throughout the pandemic weeks, especially during peak hours of 8 am- 12 noon and 6 pm – 10 pm. Electricity demand and price has been dropped by 3% and 42% respectively on average. Howev-er, overall usage patterns have not changed significantly compared to the same period last year. The predictive accuracy of the proposed model is quite effective with an acceptably smaller error despite trend change phenomena triggered by the pandemic. The model performance is comprehensively com-pared with a few conventional forecast methods, Support Vector Machine (SVM) and Regression Tree (RT), and as a result, the performance indices RMSE and MAE have been improved using the proposed LSTM model.
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