Abstract:
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.
