# Simulating Intraday Electricity Consumption with ForGAN
## 3 Experimental Setup: Data Description and Algorithmic Decisions  
### Dataset: Intraday Electricity Consumption  
For this study, four years of 15 min intraday electricity consumption data in Germany are considered, spanning from 1 January 2015 to 31 December 2018. The data used for training and evaluation are sourced from Smard ([[https://www.smard.de/home](https://www.smard.de/home)]([https://www.smard.de/home](https://www.smard.de/home))), the German market transparency platform for electricity consumption, and they can be downloaded freely.  
The dataset shows some regularity on a large scale and seemingly random fluctuations on a small scale, as illustrated in Figure 4. Specifically, there is the _weekend effect_, characterized by lower consumption levels on weekends compared to weekdays (see Figure 5). Additionally, the data display clear _seasonal patterns_, with consistent daily cycles of electricity usage. For instance, consumption peaks during daytime hours and declines during nighttime, reflecting typical human activity and business operations (see Figure 6). The regularity and seasonality in the data provide a structured framework that ForGAN can leverage to learn and predict the conditional distribution of future consumption effectively.  
The box plots in Figure 7 highlight the variation in electricity consumption across different time intervals. It is evident from these plots that electricity consumption decreases significantly during weekends and gradually increases over the four-year period. In addition, there is a slight rise in consumption during the winter months, suggesting higher  
Figure 4: Fifteen-minute electricity consumption in megawatt hours in Germany in 2015–2018.  
Figure 5: Fifteen-minute electricity consumption in megawatt hours over a randomly selected week.  
electricity usage for heating purposes. The box plots also show that there are no outliers. This could also be confirmed by using the _interquartile range (IQR)_ method.  
The _heatmap_ in Figure 8 visualizes the daily average 15 min electricity consumption for each day in 2015-2108. The color of each box in the heatmap indicates the height of the consumption, as given by the color bar on the right side of the graph.  
The heatmap reveals strong correlations in electricity consumption based on seasonal changes and weekly cycles. These correlations are consistent across the four-year period, although they do not seem to be fully predictable. The absence of significant outliers in  
Figure 6: Fifteen-minute electricity consumption in megawatt hours over a randomly selected day (5 September 2018).  
Figure 7: Box plots for the average 15 min electricity consumption in megawatt hours in Germany in 2015–2018 on the basis of years, weekdays and months (from top).  
the heatmap further suggests stable and reliable data trends. In general, there is a lower correlation between the weekends compared to weekdays across different months. However, this pattern is not uniform, as some months (particularly those with extreme weather conditions) show higher overall consumption, slightly weakening the drop typically seen on weekends. Hence, while weekends generally display lower electricity usage, the amount of this reduction can vary based on the month.