## 5 Results and Discussion  
### Conventional Time Series Model Testing and Analysis  
To obtain the best result, the parameters were selected based on the minimum MAPE for each forecasted value. For the initial input for the time series model, the time lags were the complete set of lag values. Table 1 shows the parameters for the selected model.  
Based on the lag load, considering that the error varied for the MA and WMA models, a maximum of 100 days of lag was considered for analysis, and the minimum MAPE was obtained when the model considered the previous values of 4 days. Averaging a higher lag value did not affect the MAPE. In the case of the WMA model, considering the MA model as a benchmark, the model considered 4 days for the maximum lag and weight. The WMA model was run through a simple exhaustive search algorithm of 10 \(\times\) 4 iteration points to find the 4 optimum weight values. The variation was only performed for one decimal point so that the sum of the weight equaled one, as dictated by Equation (3). Likewise, for exponential smoothing and [PERSON]'s method, the modeling again followed an exhaustive search algorithm to find the optimal value. The search ranged from 0 to 1 with 2 decimal points (i.e., a total of 100 iterations was performed). From the parameters selected from the MA, WMA, and [PERSON]'s method models, it was indicated that previous values had a lower effect on the forecasting. Similarly, the value of beta was lower, indicating no effect of the trend between the lag values.  
Analysis of the time series forecasting method was conducted using forecasted data for two days, as the study on the data indicated variation in the load demand for weekdays  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Model** & **Parameters (Weekdays)** \\ \hline
1 & Moving Average & Lag load = 4 (4-day lag) \\
2 & Weighted Moving Average & Lag Load = 4 \\
3 & Exponential Smoothing & \(\alpha=0.15\) \\
4 & Holt’s (Double Exponential) & \(\alpha=0.14,\ \beta=0.08\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter selection of the time series model.  
and weekends. The first forecasting was performed for a normal weekday (12 April 2019), specifically Friday, and the second was performed for a weekend (13 April 2019) on a Saturday. From Figure 10a,b, it can be seen that the models could maintain the pattern of the load. From 12:00 a.m. to 7:00 a.m., the load had a similar pattern for both weekdays and weekends. However, the electricity demand during the daytime from 8:00 a.m. to 9:00 p.m. was comparatively lower on weekends than on weekdays such that the model performed better for working days.  
The accuracy in the forecasting of the weekday load and weekend load showed a huge difference for all the time series models. Table 2 shows the MAPE (%) of each time series model. Here, each model archived satisfying results for the weekdays. The comparison shows that the MAPE achieved for the MA model was comparatively lower than the other models for working days. The results indicate that the load on each working day was similar to the previous day's load. However, when observing the MAPE on the weekends, the best result obtained was 12.57% for [PERSON]'s method. Even though the system was able to grasp the daily load pattern as observed in Figure 10b, the average load demand on the weekend was comparatively lower, setting the model forecast value off the course during the weekend. Likewise, with lower values for alpha in both the exponential model and [PERSON]'s method, the effect of the previous load lags was lower. This shows that higher order lag had a lower influence on the current load demand.