**4. Proposed UCLF Forecasting System**

Figure 7 presents the proposed UCLF forecasting system. The power stations monitor their plant's performance and report this locally at the station and centrally. These data are then stored in a central database. The UCLF data are part of these stored power station data. A record of the power station units that are on planned outages, PCLF, for maintenance or refurbishment is also stored centrally. These PCLF data are then provided by a central planning department in conjunction with the central operations department. The planning department also provides the installed capacity data to the central database. The system operator or an equivalent department would then provide the demand data. The data are pre-processed, and the variables are then consolidated for input into the deep learning (DL) ensemble UCLF forecasting module. The DL ensemble UCLF forecast module contains a DL ensemble model that forecasts the UCLF. The UCLF forecast is then stored and used by the planning, operations, and system operator. The DL ensemble model is developed and tested offline, and then deployed in the system. The UCLF forecast data together with the

actual UCLF data are then used by a model performance evaluation module to periodically check if the model's accuracy is still acceptable based on the utility's requirements.

**Figure 7.** Proposed deep learning UCLF forecasting system.
