*2.4. Ensemble*

Ensembles of models of the three techniques used in this study, LSTM-RNN, OP-ELM, and DBN, are investigated for UCLF forecasting. Ensemble models are a combination of multiple models to try to achieve better performance than that of the individual models. There is a number of different ways that models can be combined to form an ensemble [30]. Figure 3 shows a summary of the aggregate method, which is commonly used in regression problems. Here, models operate in parallel, and their outputs are aggregated to obtain the ensemble model's output. The aggregate ensemble model output, *Oϕ*, can be written as (16). Here, *Omk* is the ensemble model's *kth* output for models *m*1, *m*<sup>2</sup> ... *mn*, and *n* is the number of models used to develop the assembly model. The equally weighted method was used, where each model's output into the ensemble model is given an equal weight.

$$O\_{\mathcal{P}} = \frac{1}{n} \sum\_{k=1}^{k=1} O\_{mk} \tag{16}$$

**Figure 3.** Summary of the aggregate ensemble method.

#### **3. Experimental Setup**

This section presents the experiment setup via two sub-sections. The first sub-section presents the South African coal generation plants overview. The second sub-section presents the experimental approach.
