| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 82 === Forecasting of electric load demand is quite an important
subject to load management personnel. Earlier electric flow
analysis conducted by accurate short-term load forecasting
increases the power transmission efficiency and reduces the
response time of power generators. Accurate short-term load
forecasting will be helpful for higher electric dispatching
quality. A wide variety of applications for neural network on
electric power system have been reported in the literature such
as generator emergency treatments,problem diagnosis, harmonic
wave identification, and so on. In this thesis, a neural
network model is constructed depending on the actual power
demand condition of Taiman area and is expected to be a valid
model for load demand forecasting. The proposed network is
designed to provide one-day ahead forecasting of the peak load,
valley load and 24 hours load demand based on the historical
load data and the time and the weather variables. Five years
historical data(1986-1990) was used to train the network in the
study and one and half a years data (1991-06.1992) was used to
demonstrate the effectiveness of the proposed network. The
result shows about 2% average or less mean absoluate percentage
error by the proposed model. The overall performances of the
developed networks indicates that it could be an effective
method to short-term load demand forecasting.
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