# Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling
## 4 Comparisons
### Description of the Data for the Modeling with the RBM Network  
The utilized history data of electricity consumption at each hour and temperature observations of the International Organization for Standardization (ISO) in the Great Britain city [38]. For the electricity consumption modeling, we consider the next eight inputs with data of dimensions \(52606\times 8\) for the modeling of the RBM network:  
* The temperature of the dry bulb. * The dew point. * Hour of the day. * Day of the week. * A mark indicating if this is a free or a weekend day. * Average load of the past day. * The load of the same hour in the past day. * The load of the same hour and day in the past week. Additionally, we utilize one target with data of the load of the same hour and day in the actual week of dimensions \(52,606\times 1\) for the modeling of the RBM network. The analysis of this sub-section is used to show that the \(52,606\) complete data containing eight inputs with data and one target with data are transcendent for the modeling with a RBM network. Since each measure of the \(52,606\) complete data is updated at each hour (h), the \(52,606\) complete data of each input and target are physically related to h.  
The \(52,606\) complete data are split in two parts before to the modeling procedure; the first \(43,834\) dataset is used for the training, being called training data, and the last \(8772\) dataset is used for the testing, being called testing data. With the training data of (\(43,834\times 8\)) for the inputs and (\(43,834\times 1\)) for the target, we train the RBM network for the electricity consumption modeling. After of the training stage of the RBM network, with the testing data of (\(8772\times 8\)) for inputs and (\(8772\times 1\)) for target, we test the RBM network for the electricity consumption modeling. Table 1 shows the numerical values for the eight inputs with training data and one target with training data, with count as the number of training data, mean as the mean in the training data, std as the standard deviation of the training data, min as the minimal value of the training data, max as the maximum value of the training data, BulbT(A\({}^{\circ}\)F) as the temperature of the dry bulb, dewPoint(A\({}^{\circ}\)F) as the dew point, Weekend as the mark indicating if this is a free or a weekend day, PaverageLoad in MWh as the average load of the past day, LoadPreviousD in MWh as the load of the same hour in the past day, LoadPreviousW in MWh as the load of the same hour and day in the past week, and ActualLoad in MWh as the load of the same hour and day in the actual week. Figure 7 shows the Pearson relation coefficient called r. The training data are analyzed in groups by two training datasets as follows:  
* If r = 1, there is a perfect positive relation between two training datums. * If 0 \(<\) r \(<\) 1, there is a positive relation between two training datums. * If r = 0, there is not relation between two training datums. * If \(-\) 1 \(<\) r \(<\) 0, there is a negative relation between two training datums. * If r = \(-\)1, there is a perfect negative relation between two training datums. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Count** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline BulbT & 43,834 & 50,0716 & 18.5104 & \(-\)7 & 36 & 51 & 65 & 96 \\ dewPoint(A\({}^{\circ}\)F) & 43,834 & 38.3980 & 19.6439 & \(-\)24 & 24 & 40 & 55 & 75 \\ Hour & 43,834 & 12.4984 & 6.9224 & 1 & 6 & 12 & 18 & 24 \\ Day & 43,834 & 4 & 2.0003 & 1 & 2 & 4 & 6 & 7 \\ Weekend & 43,834 & 0.6890 & 0.4629 & 0 & 0 & 1 & 1 & 1 \\ PaverageLoad & 43,834 & 15,218.2727 & 297.5212 & 9152 & 12,950 & 15,411 & 17,085 & 28,130 \\ LoadPreviousD & 43,834 & 15,214.8604 & 2975.7433 & 9152 & 12,938.25 & 15,418 & 17,087.5 & 28,130 \\ LoadPreviousW & 43,834 & 15,211.0955 & 1739.9369 & 509.5833 & 14,053.5520 & 14,953.0416 & 16,125.9791 & 23,479.4583 \\ ActualLoad & 43,834 & 15,214.9935 & 2976.1711 & 9152 & 12,936 & 15,420 & 17,089 & 28,130 \\ \hline \hline \end{tabular}
\end{table}