Abstract:
Truncated Electricity is an essential resource for industry, transportation, and
communication; thus, it is vital to understand the patterns of electricity usage for the
well-being of society and economic growth. A time series approach is employed to
model and forecast the electricity demand in the Ampara district of Sri Lanka from
January 2019 to November 2023. This study aims to elucidate the complex pattern of
electricity consumption across sectors by examining multiple consumption categories
including domestic, religious and charitable, public purpose, industrial, government
universities, and government hospitals and schools. The study highlights the importance
of the domestic category in particular, where consumption varies significantly due to
recent increases in unit prices. The study uses unit root tests, time series plots, and
descriptive statistics, to determine data stationarity and identify consumption trends.
Electricity usage in the domestic category is predicted using the Seasonal
Autoregressive Integrated Moving Average (SARIMA) model. Thirteen SARIMA
models are thoroughly evaluated using log-likelihood, AIC, SC, HQC, and number of
significant coefficients. The SARIMA(1,0,0)(1,1,0)12 is chosen as the best model
because of the absence of ARCH effects. Extensive validation on an independent testing
dataset improves the model's accuracy and diagnostic tests confirm its reliability. This
research is beyond its capacity for prediction and provides a reliable forecasting model
with real-world implications for future electricity planning and policy development in
the Ampara district. The knowledge gained from this research offers stakeholders and
decision-makers in the regional energy sector with useful information.
