*3.2. Feature Variable Selection*

Many predicting features affect energy consumption. For instance, date meta-data features, such as the time of day, type of day, season, and weather conditions, correlate with energy consumption. The selection of such input parameters is critical to the performance of the predictive model. Uncorrelated or associated parameters may not only lead to model overfitting but affect the performance of the predicting model [42,43]. The set of variables for this study, described in Table 1, were chosen due to their correlation with energy consumption.



Figures 4 and 5 show the relationship between the temperature and cloud cover with the demand load. There is a positive correlation between the selected predictors and the demand load. With different load profiles for weekdays and weekends, it imperative to mention the effect that temperature and cloud cover have on the demand load. During temperate seasons, load demands are not as high as during hot seasons because higher energy consuming devices, such as air-conditioning and ventilating, will not be used as much compared with during hot seasons like summer.

**Figure 4.** Energy consumption variations with temperature.

**Figure 5.** Energy consumption variations with cloud cover.
