Forecast strategy using an adaptive fuzzy classification algorithm for load

Author

Bretschneider, P. ; Rauschenbach, T. ; Wernstedt, J.

Author_Institution

Tech. Univ. Ilmenau, Ilmenau, Germany

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

4795

Lastpage

4798

Abstract

Forecast is applied in many fields. The determination of system signals, states or parameters of technical and not technical processes allows the solution of higher level tasks, for instance the optimization of complex systems or the generation of decisions. Basic methods of prediction are signal models, analytical, symbolic, cognitive models and state-space models [1], [2], [3], [4], [5]. The problem definition and the process character influence essentially the choice of the model type. The current state of forecast methods is demonstrated in figure 1.

Keywords

fuzzy systems; load forecasting; optimisation; prediction theory; adaptive fuzzy classification algorithm; analytical prediction model; cognitive prediction model; complex system; load forecasting strategy; optimization; signal prediction model; state-space prediction model; symbolic prediction model; Adaptation models; Analytical models; Classification algorithms; Load modeling; Mathematical model; Predictive models; Wind forecasting; Fuzzy Classification; Intelligent Forecasting; Multi Step Model; Short Term Load Forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7100094