# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 1 Introduction  
Electricity consumption profiles worldwide have shifted in magnitude and daily patterns due to uncertain events, such as the COVID-19 pandemic, changing climate, and adverse effects of more frequent extreme conditions. These changes have caused significant difficulties and declined accuracy for traditional short-term electricity demand forecasting (EDF) methods. Accurate forecasting simultaneously becomes more important and challenging as the penetration of renewable energy increases the bi-directional communication between the supplier and the end-users. 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 seveances 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. Figure 1 shows the monthly electricity time of use before and during the COVID-19 pandemic and the same period in 2021 and 2022, ranging from February to March, in NSW State, Australia [2]. It suggests that the electricity demand experienced significant drops during the pandemic compared with the same period in 2019. Moreover, the accuracy of the conventional models is not robust since time-varying electricity demand is more likely a non-stationary stochastic process, showing the challenges in forecasting caused by unprecedented events [3]. Table 1 lists the annual consumption forecast accuracy (performance) in five regions of Australia between 2019 and 2022. It shows the percentage differences (errors) between actual and forecast values of the published energy forecasts (-ve error implies the actual is lower than forecast by %) adopted by the Australian Energy Market Operator (AEMO 2022) [4]. 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. There are no historical events similar to the COVID-19 pandemic that can follow the consumption pattern since only months of data are available for model training and testing processes. (2) Any unknown underlying feature that affects usual usage patterns--conventional short-term forecasting models rely on long-term patterns and are not adaptive enough to learn about unprecedented events. 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. EDF has an important role in the economy and is frequently used in business planning, policymaking, and market setting [6]. Moreover, it is crucial for the smart grid operation and may pose a technical risk during unusual situations when forecast accuracy significantly declines. Consequently, the aforementioned uncertainties affect the short-term forecasting algorithm's performance. Therefore, it is imperative to improve forecasting accuracy in terms of possible error reduction and uncertainty depiction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**One-Year-Ahead Annual Operational** & **2018–2019** & **2019–2020** & **2020–2021** & **2021–2022** \\
**Consumption Accuracy (\%)** & & & & \\ \hline New South Wales & -2.0\% & -0.26\% & -1.1\% & -3.9\% \\ South Australia & -1.5\% & 2.6\% & -0.3\% & -0.8\% \\ Tasmania & 1.2\% & 2.2\% & 2.4\% & -1.3\% \\ Queensland & -3.9\% & 0.0\% & -2.4\% & -5.2\% \\ Victoria & 3.0\% & 1.3\% & -1.7\% & -8.4\% \\ \hline \hline \end{tabular}
\end{table}