## 4 Results and Discussion  
### Correlation Analysis  
In this analysis, some of the weather variables considered include relative humidity, dew point, and temperature. Relative humidity signifies the ratio of water vapor present in the air compared to the maximum amount it can hold at that temperature, expressed as a percentage. Higher humidity during warmer months amplifies the sensation of heat compared to the actual temperature [17]. These weather variables are often included together due to their inherent interdependencies.  
Table 8 presents the correlation coefficient between the load consumption and the model inputs. The correlation coefficients were computed for load and weather variables  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MAPE (\%)** & **Forecasting Capability** \\ \hline \(<\)10 & Highly accurate forecasting \\ 10–20 & Good forecasting \\ 20–50 & Reasonable forecasting \\ \(>\)50 & Inaccurate forecasting \\ \hline \hline \end{tabular}
\end{table}
Table 6: Forecasting capability based on the MAPE value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measures** & **Criteria** & **Description** & **Equation** & **No. of Equation** \\ \hline \multirow{3}{*}{**MAPE**} & Mean absolute percentage & Reflects the degree of data & \\  & error & dispersion and accurately & \\  & error & captures the actual forecasted & \(\dfrac{1}{p}\sum\limits_{i=1}^{p}\dfrac{w_{i}-\hat{w}}{w_{i}}\) & (25) \\ \hline \multirow{3}{*}{**MAE**} & Mean absolute error & Shows the mean distance between the actual and forecasted values & \(\dfrac{\sum_{i=1}^{p}|w_{i}-\hat{w}_{i}|}{p}\) & (26) \\ \cline{2-4}  & Mean square & Reflects the degree of dispersion of the dataset [63] & \(\dfrac{\sum_{i=1}^{p}(w_{i}-\hat{w}_{i})^{2}}{p}\) & (27) \\ \hline \multirow{3}{*}{**RMSE**} & Root mean square error & Captures the average error between the forecasted value and the actual value [63] & \(\sqrt{\dfrac{\sum_{i=1}^{p}(w_{i}-\hat{w}_{i})^{2}}{p}}\) & (28) \\ \hline \multirow{3}{*}{**R2**} & \multirow{3}{*}{Determination coefficient} & Determines the proportion of the variance in the dependent variable that is predictable from the independent variables [64] & \(1-\dfrac{\sum_{i=1}^{p}(w_{i}-\hat{w}_{i})^{2}}{\sum_{i=1}^{p}(w_{i}-\overline{ x})^{2}}\) & (29) \\ \cline{1-1} \cline{3-4}  & & Normalizes the RMSE by dividing it by the average of the actual values. Prone to the influence of large outliers [65] & \(\dfrac{\sqrt{\frac{\sum_{i=1}^{p}(w_{i}-\hat{w}_{i})^{2}}{p}}}{\frac{1}{p}\sum _{i=1}^{p}w_{i}}\) & (30) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive of accuracy measures for performance evaluation.  
over a 48-day period. Notably, the previous day's load exhibits a strong positive correlation with the current day's load, indicating a significant influence on the forecasted load. The relative humidity and temperature show a moderate correlation, while the dew point shows weak and no correlation with load because the correlation coefficients are less than \(\pm 0.6\), as mentioned in Table 2. Thus, the simulation model only considered the input from the previous load with a strong correlation whereas weather variables are neglected.