*4.2. Prediction Using Moving Average Data*

We also explore the possibility of improving the accuracy of the machine-learningbased electricity forecasting system by adding another feature configuration. In this subsection, we experiment with scenarios when additional features are added into machine learning, i.e., moving average (MA) data of the electricity load data. The moving average data is the electricity load that is averaged with a specific time frame range. It is possible to obtain this MA data in the implementation of the electricity load forecasting as long as realization (observation) data of electricity load can be accessed directly and fed into the machine learning forecasting system.

This subsection added three scenarios of moving average (MA) data, i.e., monthly, weekly, and daily moving average data. Monthly moving average data means that averaged electricity load data is calculated with a time frame of one month from the time series of historical electricity load data. The MA information is fed into the machine learning forecasting system. To compare how effective the addition of MA data was into the machine learning model, we performed electricity load prediction using the GRNN and SVR model with scenario-2, as shown in the previous subsection. The scenario-2 is added with monthly, weekly, and daily MA as new scenarios. Figure 9 shows the results of each scenario with MA data. From Figure 9, the scenario with monthly MA data results in worse performance than the scenario without MA.

**Figure 9.** Comparison between electricity load data (solid black line) during the period 1–20 January 2019, with results of prediction by using GRNN model (dashed red line) with various feature moving averaged (M.A.) scenarios; (**a**) scenario without M.A.; (**b**) scenario with MA-Monthly; (**c**) scenario with MA-Weekly; (**d**) scenario with MA-Daily.

On the other hand, better performance is achieved by scenarios with weekly and daily MA data. Quantitatively, each scenario's performance is summarized in Table 5 for both using GRNN and SVR model. The best performance scenario for both GRNN and SVR is the scenario with MA-daily; for the GRNN model, the best scenario gives a CC value of 0.956 and RMSE value of 28.82, whereas for the SVR model, it gives a CC value of 0.965, and RMSE value of 44.40. Note that the SVR model gives slightly better performance in terms of CC value than the GRNN model results but gives a worse performance in terms of RMSE value. Overall, the GRNN model gives better results than the SVR model.

**Table 5.** Results of prediction by using GRNN and SVR model with various scenario with Moving Average (M.A.) values; Monthly, Weekly, and Daily.


We compare prediction results using a scenario with MA-daily in Figure 10 for both GRNN and SVR models. Qualitatively, the GRNN gives better prediction, especially vertical direction errors, confirmed by RMSE values as in Table 5.

**Figure 10.** Comparison between electricity load testing data (solid black line) with results of prediction by using GRNN model (dashed red line), and SVR (dotted magenta line) for Scenario with MA-Daily; (**a**) during the period 1 January–1 August 2019; (**b**) during the period 1–20 January 2019.
