## 2 Materials and Methods
### Input Data Employed for the 24-Hour-Ahead Forecasting
Another environmental variable that affects electricity consumption is the ambient air humidity, usually reported in the form of more complex indices such as the relative humidity (RH), dew point (DP) temperature, or wet bulb (WB) temperature. However, the correlation of any one of these indices with the total load is not straightforward. On the other hand, since the effect of ambient temperature and humidity on the electricity consumption is conveyed through the heating or cooling requirements, we considered, as a good practice, to correlate the total daily load in GWh with the heating degree-days instead. These are routinely reported on a daily basis by all weather stations. This correlation is presented in Figure 10 for a typical weather station in the Athens area during 2021. Figure 9: Correlation of the total daily electricity load in GWh with the daily mean temperature (DB) of Psychico weather station (year 2021). As seen in Figure 10, the correlation of the total daily load with the daily heating degree days during the heating season is significantly better than the correlation with ambient temperature seen in the previous Figure. This hints at a possibly better training ability of the machine learning models to be employed in the predictions. Moreover, cooling degree days are also reported on a daily basis by all weather stations since they give an estimate of the necessary energy consumption for space cooling during the summer. Again, a correlation of the total daily load in GWh with the cooling degree days of a typical weather station in Athens, shown in Figure 11 reveals a clear positive correlation whenever five cooling degree days are exceeded daily. As already observed for the cooling season, the correlation coefficient of the total daily load with the daily cooling degree days is significantly higher than the correlation with the heating degree days. Based on the above findings, in the current work, we preferred to use the daily heating and daily cooling degree days instead of the DB and the DP temperature. The required weather data are significantly more simple and easy to acquire since they refer to just two daily values instead of the two 24 h vectors required by the usual practice. Moreover, the daily weather forecast required for the day-ahead forecast requires just a  
Figure 11: Correlation of the total daily electricity load in GWh with the daily cooling degree days (year 2021). Figure 10: Correlation of the total daily electricity load in GWh with the daily heating degree-days (Psychico station, year 2021). single value (daily heating or cooling degree days) instead of 48 values, involving the forecasted hourly values of DB and DP temperatures. To summarize our model training approach, the following input parameters are selected for training:  
* Heating degree days (daily for a representative meteorological station);
* Cooling degree days (daily for a representative meteorological station);
* Hour of day;
* Day of the week;
* Holiday/weekend indicator (0 or 1);
* 24 h lagged load (hourly resolution);
* 168 h (previous week) lagged load (hourly resolution).