*5.2. LSTM-RNN Results*

LSTM-RNN models were developed using the different variables per respective experiment. The performance of the different LSTM-RNN models was observed. The lowest obtained year-ahead UCLF forecast errors, per experiment, are captured in Table 3.

**Table 3.** LSTM-RNN experiments results.


A model with 511 hidden units and Experiment 1 variables had the lowest errors. Here, an sMAPE of 7.95%, MAE of 9.14%, and RMSE of 11.42% were achieved. Higher errors were observed in Experiments 4 and 5, where the installed capacity was excluded. These errors were approximately twice the errors in Experiment 1. A statistical significance test was conducted to determine if the results with the lowest errors in each experiment were significantly different from the results with the overall lowest errors. The results were found to be statistically different from each other as a *p*-value of less than 0.05 was observed in all four cases. The obtained *p*-values are captured in Table 4.

**Table 4.** LSTM-RNN models' lowest errors statistical significance test.


#### *5.3. DBN Results*

The DBN models were developed as discussed in Section 3. The errors for the models' year-ahead UCLF forecast results were observed and the lowest obtained errors per experiment are captured in Table 5. A model with nine hidden nodes developed using all the variables was found to achieve the lowest errors, with an sMAPE of 9.74%, MAE of 11.52%, and RMSE of 13.74%. Experiments 4 and 5 showed an increase that was approximately three times the errors observed in Experiment 1.

The statistical significance test was conducted as described in Section 3.5 and the test result showed that the forecasting results were significantly different. Table 6 shows the statistical significance test results. The *p*-value can be seen to be less than 0.05 in each case, indicating a significant difference in the respective cases.


**Table 5.** DBN experiments results.

**Table 6.** DBN models' lowest errors statistical significance test.

