Accurate short term load forecasting plays a very
important role in power system management. As electrical load
data is highly non-linear in nature, in the proposed approach,
we first separate out the linear and the non-linear parts, and
then forecast the load using the non-linear part only. The Semiparametric
spectral estimation method is used to decompose a
load data signal into a harmonic linear signal model and a nonlinear
trend. A support vector machine is then used to predict
the non-linear trend. The final predicted signal is then found by
adding the support vector machine predicted trend and the linear
signal part. With careful determination of the linear component,
the performance of the proposed method seems to be more
robust than using only the raw load data, and in many cases
the predicted signal of the proposed method is more accurate
when we have only a small training set.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:tut/oai:encore.tut.ac.za:d1000836 |
| Date | 23 September 2009 |
| Creators | Jordaan, JA, Ukil, A |
| Publisher | IEEE Africon |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | |
| Rights | ©2009 IEEE |
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