It is crucial to plan the electricity supply to match the future demand since
electricity has become a dominant utility. Sri Lanka as a developing country, has
over 98% of households electrified, which sometimes suffer from interruptions in
supply. This study aims at forecasting monthly electricity consumption in Sri
Lanka by considering the influence of weather patterns. Rainfall, humidity, and
temperature are the three main weather parameters found to affect the electricity
demand. We compared eight forecasting approaches including four econometric
models and four algorithmic forecasting methods in forecasting monthly
electricity consumption. Twenty meteorological stations were considered to
spatially interpolate the weather data using the Inverse Distance Weighted (IDW)
interpolation method. Results revealed that Autoregressive Distributed Lag
(ARDL) model which incorporates the weather patterns as predictors outperforms
in forecasting the monthly electricity consumption compared with all other
forecasting approaches.
Keywords
Autoregressive distributed lag model, Electricity consumption forecasting, Inverse distance weighted interpolation, Missing value imputation Weather impact