Short-term load forecasting (STLF) is an important part of the power generation
process. For years, it has been achieved by traditional approaches stochastic like
time series / but, new methods based on artificial intelligence emerged recently in
literature and started to replace the old ones in the industry. In order to follow the
latest developments and to have a modern system, it is aimed to make a research
on STLF in Turkey, by neural networks. For this purpose, a method is proposed to
forecast Turkey&rsquo / s total electric load one day in advance. A hybrid learning scheme
that combines off-line learning with real-time forecasting is developed to make
use of the available past data for adapting the weights and to further adjust these
connections according to the changing conditions. It is also suggested to tune the
step size iteratively for better accuracy. Since a single neural network model
cannot cover all load types, data are clustered due to the differences in their
characteristics. Apart from this, special days are extracted from the normal
training sets and handled separately. In this way, a solution is proposed for all
load types, including working days, weekends and special holidays. For the selection of input parameters, a technique based on principal component analysis
is suggested. A traditional ARMA model is constructed for the same data as a
benchmark and results are compared. Proposed method gives lower percent errors
all the time, especially for holiday loads. The average error for year 2002 data is
obtained as 1.60%.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/627505/index.pdf |
| Date | 01 January 2003 |
| Creators | Topalli, Ayca Kumluca |
| Contributors | Erkmen, Ismet |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0069 seconds