A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management
system. Reliable load forecast technique will help the electric utility to make unit
commitment decisions, reduce spinning reserve capacity, and schedule device
maintenance plan properly. Thus, besides being a key element in reducing the
generation cost, power load forecast is an essential procedure in enhancing the
reliability of the power systems. Generally speaking, power systems worldwide are
using load forecast as an essential part of off-line network analysis. This is in order
to determine the status of the system, and the necessity to implement corrective
actions, such as load shedding, power purchases or using peaking units.
Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and
168-hours ahead is a necessary daily task for power dispatch. Its accuracy will
significantly affect the cost of generation and the reliability of the system. The
majority of the single variable based techniques are using autoregressive-moving
average (ARMA) model to solve the STLF problem.
In this thesis, a new AR algorithm especially designed for long data records as a
solution to STLF problem is proposed. The proposed AR-based algorithm divides
long data record into short segments and searches for the AR coefficients that
simultaneously model the data with the least means squared errors. In order to verify
the proposed algorithm as a solution to STLF problem, its performance is compared
with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA
(SARIMA). In addition to the parametric algorithms, the comparison is extended
towards artificial neural networks (ANN). Three years data power demand record
collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC,
between the beginning of 2005 and the end of 2007 are used for the comparison. The
results show the potential of the proposed algorithm as a reliable solution to STLF.