*4.1. Prediction Using Weather Data*

This subsection proposes multiple scenarios for feature input to design a machinelearning-based electricity load forecasting system. We design scenarios that add one-by-one weather parameters, from high to low CC value, as feature input for two machine learning models, i.e., the GRNN and SVR. Besides weather parameters, customer characteristics also significantly affect electricity load consumption, as shown in Figure 2. We include two characteristics of electricity customers in Bali island, i.e., hourly and daily characteristics, illustrated in Figure 2. The hourly characteristics are represented as values from 1 to 24 that represent hours, whereas for the daily characteristics, there are values from 1 to 7 that represent day number. These two customer characteristics are included as scenario-1 in Table 2. For other scenarios, i.e., scenarios 2 to 6, we added one-by-one weather parameters, from high to low correlated weather parameters, as shown in Table 2.


**Table 2.** Scenarios to investigate effects of each weather parameter as feature input for the machine learning.

For training data for the machine learning models, we use one year data, i.e., during 2018, to forecast 1-month electricity load data, i.e., January 2019. Using features configuration scenarios as shown in Table 1, we perform electricity load forecasting using the GRNN model, as shown qualitatively in Figure 8. Here, we can see qualitatively that scenario-2 in Figure 8b. gives the best prediction compared to other scenarios. The scenario-2 consisted of hourly and daily characteristics with 2 m temperature as input for the machine learning model. Adding additional weather parameters features such as scenario-3 to -6 results in worse prediction performances, as shown qualitatively in Figure 8c–f.

**Figure 8.** Comparison between electricity load data (solid black line) during the period 1–25 January 2019, with results of prediction by using GRNN model (dashed red line) with various feature scenarios; (**a**) scenario-1; (**b**) scenario-2; (**c**) scenario-3; (**d**) scenario-4; (**e**) scenario-5; and (**f**) scenario-6.

We also optimize the GRNN and SVR model parameter settings to give the best prediction. For the GRNN, there is only one parameter to be optimized, i.e., the "spread" parameter. The spread parameter is optimized by varying its value, as shown in Table 3. Table 3 shows results of various values of spread parameter of GRNN model for predicting scenario-2. From this table, the spread value of 0.50 gives the best performance. We also optimized parameter settings in the SVR model. The best result is obtained with radial

basis function kernel, regularization parameter C value of 100, kernel coefficient *γ* of 2,  value in SVR model is 0.1, and polynomial degree 3.


**Table 3.** Results of various value of parameter Spread in the GRNN model for scenario-2.

Not only using the GRNN, we also perform prediction by using the SVR model, in which results of prediction by using two models are summarized in Table 4. Here, the best scenario for the GRNN model is obtained by scenario-2, which results in a CC value of 0.937 and a root mean square error (RMSE) value of 41.72. For the SVR model, the best scenario is obtained by scenario-3, i.e., with weather parameter temperature and net solar radiation, resulting in a CC value of 0.934 and an RMSE value of 48.88. Note that the RMSE value of the best scenario obtained by using the GRNN model is lower than the SVR model. It is also the same with the CC value; the GRNN model gives slightly better performance than the SVR model.

**Table 4.** Results of prediction by using GRNN and SVR model with various weather parameter scenarios, as described in Table 2.

