Forecasting of Nigeria’s Energy Demand: A Comparative Study of ARIMA, RNN, and LSTM Models

The present study aimed to forecast future energy demand in Nigeria for a five-year period using three predictive models: ARIMA, RNN, and LSTM. The research explored historical energy consumption data from 2015 to 2022, collected from various distribution companies in Nigeria. The data underwent pre-processing to handle missing values and outliers, and stationary analysis was conducted to ensure the suitability of the models. The dataset was then split into training and testing sets using a sliding window technique. The models were trained and evaluated based on performance metrics such as RMSE, MSE, and MAPE. The findings revealed that the RNN model outperformed both ARIMA and LSTM in predicting energy demand, exhibiting the lowest error scores. The study demonstrates the effectiveness of advanced deep learning models, like RNN, for precise and accurate energy demand forecasting in Nigeria over the next five years.