Abstract:
Most of the time electricity cannot be stored, it should be generated as soon as it is
demanded. Therefore, electricity demand forecasting is a vital process in the planning of
electricity industry and the operation of electric power systems. Two major scenarios
should be considered when forecasting the electricity demand. They are short term and
long term forecasting scenarios. The short term scenario is more critical since many
features have to be considered. In this research study, deep learning techniques such as
Recurrent Neural Network(RNN), Long Short Term Memory (LSTM) and Convolutional
Neural Network (CNN) were considered for electricity demand forecasting of Sri Lankan
demand profile. Further, the results of deep learning approaches were compared with
traditional techniques such as Linear Regression, Lasso Regression, Light Gradient
Boosting Model (LGBM) and Random Forest Regressor. It was found from our studies
that LSTM based approach performs better than other approaches.
