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

Abstract

The smart city integrating the smart grid as an integral part of it to guarantee 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 introducing new uncertainties in short-term demand forecasting since its result depends on the recent usage as an input variable. Addressing this challenging 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 proposed 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. However, 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 compared 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.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

“…Both the spatial and temporal electricity demand patterns have changed in comparison with the non-pandemic period once the majority of people started to work from home, and the forced closure of industries slowed down other commercial activities [1,2]. These changing working conditions were eventually reflected in electricity grid planning, demand scheduling, renewable source integration, and spot pricing.…”

“…As can be seen from the table, forecast accuracy declined mostly over the pandemic period 2020-2022. A few major factors contribute to the steep decline in forecasting accuracy [1,5]. (1) Limited data-small amounts of historical data generally decrease model accuracy.…”

“…Optimal decisions can be obtained by improving forecast accuracy and quantifying uncertainty. Any uncertain events, such as the current COVID-19 pandemic, have brought much more effective practices for utility demand forecasting [1].…”

“…The socioeconomic severances affected total levels of electricity consumption, demand, price, and usage trends worldwide during the pandemic [1]. In Australia, the nationwide restrictions, which started in mid-March 2020 to control the spread of COVID-19, also decreased electricity demand.…”

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model

“…On the basis of Scopus, in addition to the previous reference [9] , three more works were found. In [36] , despite mentioning that the pandemic had socioeconomic impacts, the focus of the article is to propose a model for forecasting electricity price and demand based on the LSTM Deep Learning method considering recent demand trends in the Australian electricity market. In [37] the authors highlight the variation in energy demand as a direct and measurable impact of the pandemic on the electric sector and present a methodology to assess the effects of blockages on the European electric system.…”