Abstract:
Due to the increasing population, industrialization, and extensive use of technology, there has
been a steady increase in the demand for electric energy. Accurate prediction of electric energy
consumption plays a crucial role in optimizing power generation, distribution, and ensuring a
stable power supply. However, there is a lack of study conducted on prediction of electric
energy consumption in Wolaita Zone. The traditional methods used by previous researchers
for forecasting are often limited in their ability to capture the complex patterns of electric
energy consumption. In this study, researchers aimed to overcome these limitations by
employing machine-learning algorithms to analyze historical energy consumption data and
extract meaningful and useful patterns and trends. To achieve this objective the mixed research
approach (both qualitative and quantitative) and experimental research design where
employed. By achieving these objectives, this study will contribute to the advancement of
energy consumption forecasting techniques and enable the stakeholders to make an informed
decisions regarding energy generation, distribution, and pricing. The regression based machine
learning-algorithm such as, Random forest; Extreme Gradient Boosting (XGBOOST), RNN,
LSTM and GRU were employed for prediction of electric energy consumption. Based on the
experiment-conducted recurrent neural network model suitable and outperforms others with
R-squared 0.712, MAE of 0.021, MAPE 0.678% and MSE of 0.243.
