The limitations of traditional deep learning models in processing vast volumes of data and modelling
complicated temporal dependencies make it difficult to effectively satisfy these objectives for short-term
load forecasting (STLF). This chapter utilises deep learning, which enables the following: k-means
clustering to comprehend hourly electricity demand load trend, extraction of complex features with nonlinear
interactions that impact electricity demand load, handling of long-term dependencies through
the modelling of temporal hierarchies in the time series data via long short-term memory, and recurrent
neural network to capture dependencies across time steps. The performance of the state-of-the-art hourly
demand forecast models with the k-means variant is compared with that of the RNN-LSTM-CNN-copula.
Furthermore, it is noted that RNN-LSTM-CNN-copula is a viable deep learning model for energy consumption
forecast problems because of its capacity to learn the spatio-temporal dependencies in the
hourly electrical demand load data, which results in an accurate energy demand forecast.

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