The problem is the model’s reliability, accuracy, and meaningfulness to convince decision-makers of the

actions to be taken when seasonality is one of the features in the findings of the existing deep learning

forecasts. The purpose of this chapter is to come up with a novel dual-stream hybrid architecture that is

capable of predicting electricity demand and assessing its accuracy levels by benchmarking it with individual

architecture model’s forecasting accuracy levels using out-of-sample time series. The approach

in this chapter uses time series and convolutional neural network (CNN)-based long short-term memory

with various configurations to construct a forecasting model for short- to medium-term aggregate load

forecasting. The obtained results show that the TBATS-CNN-LSTM-based model has shown high accuracy

as compared to the base learner, and the model is optimised with hyperparameter tuning. Only optimally

selected time-lag features captured all the characteristics of complex time series in South Africa.

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