| dc.description.abstract | —The national electricity grid system must be
kept in balance between sufficient supply to meet the
demand and minimization of the cost by alleviating
excessive generation. The forecasts made from the
historical electricity generation cost data can support the
national grid system in this regard. Present study suggests
a statistical time series model for forecasting the Unit Cost
(UC) of electricity generated by fossil fuel power plants
using Auto Regressive Integrated Moving Average (ARIMA)
technique. It was conducted as a case study in a
Diesel/Heavy Fuel Oil (HFO) power plant in Sri Lanka which
consists of two sub power stations. The model was
developed and validated using 80% and 20% of monthly
data from selected power plant from January 2013 to June
2018. ARIMA (1,1,0) and ARIMA (2,1,2) were selected as
the best models with the lowest Akaike Information
Criterion (AIC) for Station 1 and Station 2 respectively,
from among many candidate models that were evaluated
by the investigation of ACF and PACF of the series. The
forecasting accuracy of ARIMA (1,1,0) and ARIMA (2,1,2)
models was measured with Mean Absolute Error (MAE)
values (2.431 and 0.717) and Root Mean Square Error
(RMSE) values (3.403 and 0.927).When comparing the UC
of both stations, the forecasting values shows that UC of
Station 1 are quite greater than Station 2 values and it is
also relevant to past years’ cost data. | |
