Short-term load forecasting using system-type neural network architecture.

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dc.contributor.advisor Lee, Kwang Yun, 1942- Du, Shu, 1984-
dc.contributor.other Baylor University. Dept. of Electrical and Computer Engineering. en 2009-08
dc.description.abstract This thesis presents a methodology for short-term load forecasting using a system-type neural network based on semigroup theory. A technique referred to as algebraic decomposition is used to decompose a distributed parameter system into a semigroup channel made of coefficient vectors and a function channel made of basis vectors. The actual load data is preprocessed by regression to become better correlated to daily time and temperatures. A rearrangement method based on the hourly temperature is developed to solve the problem of the roughness of the coefficient vector in the seimigroup channel. Interpolation or extrapolation of coefficient vector can be achieved for each hour using the historical temperatures and the temperature forecast. Recombination of the basis vector and predicted coefficient vector will give the next-day load forecasting. Load data from New England Independent System Operator is used to verify the capability of the proposed approach. en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact for inquiries about permission. en
dc.subject Electric power plants -- Load -- Forecasting. en
dc.subject Electric power consumption -- Forecasting. en
dc.subject Neural networks (Computer science) en
dc.title Short-term load forecasting using system-type neural network architecture. en
dc.type Thesis en M.S.E.C.E. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Engineering. en

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