| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 81 === Forecasting of power demand has long been a subject of interest
to facilities planning and load management personnel. Accurate
load forecasting provides a basis for effective system
planning. In this thesis, a multilayer neural network based on
the widely used Backpropagation learning algorithm is proposed
for the short-term load forecasting of Taiwan power system. The
proposed network is designed to provide a one-day ahead
forecasting of both the total daily load and the peak power
load based on the time and weather related factors. The load
types were categorized into the normal working days, weekend
days, and holidays patterns. To demonstrate the effectiveness
of the proposed network, an extensive study was performed using
two years load data (1991-1992). Forecasting accuracy is
evaluated throughout a whole year to take into account the
seasonal effects on the accuracy of the proposed model. The
absolute percentage error of the proposed network was below 2%
for the weekday and weekend models. The overall performances of
the developed network indicates that it could be an efficient
and accurate method for short-term load forecasting.
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