Abstract:
Sri Lanka’s electricity demand is growing day by day. Planning to increase the electricity
supply to meet future demand is a very difficult task. Therefore, it is important
to know the future demand for uninterrupted power supply. Many past studies have
considered the correlation between weather factors and electricity demand to predict
accurate demand value. Therefore, the objective of this study is to forecast the monthly
electricity demand in Sri Lanka, considering the influence of weather patterns. Rainfall,
humidity, and temperature weather parameters are considered along with historical
monthly demand data. The most important weather variables are identified based on
correlation with electricity demand data. Several techniques have been used to forecast
electricity demand during the last decade. But the problem is that those studies did not
focus on past weather data along with electricity demand data. Most studies focused
only on historical electricity demand data. This study fills that gap. In this study, Vector
Auto Regression (VAR) and Long Short-Term Memory (LSTM) models were applied
to forecast monthly electricity demand regionally in Sri Lanka. Among them, the VAR
model yielded a lower value for Root Mean Square, Mean Square Error, Mean Absolute
Error, and Mean Absolute Percentage Error. Based on that, the VAR model has been
selected as the best-fit model for monthly electricity demand forecasting using weather
variables.
