A New Spatial Forecasting Method for Distribution Network Based on Cloud Theory
Xin Gao · Proceedings of the CSEE · 2006
A new spatial load forecasting(SLF) model for distribution network is proposed. Cloud theory is introduced into SLF in this model, and the knowledge representation based on cloud model is used to integrate the fuzziness and randomness of qualitative concept, which constitutes the mapping between qualitative and quantitative analysis, conquers the halfway of conventional fuzzy theory. The cloud theory, Attribute Oriented Induction (AOI) and rough set are integrated to make transition of spatial attribute information between qualitative and quantitative analysis, spatial data discretisation and decision rules mining so that it conquers the subjectivity of fuzzy sets parameters and fuzzy system rules choice that based on conventional fuzzy sets spatial load forecasting .The complementary of cloud theory and rough set is also used to enhance the knowledge discovery ability. The uncertain reasoning based on cloud theory is used to calculate the grading to adaptability of every small area land-use style ,which make the reasoning result more reasonable and actual. The small area redevelopment criterion based on this model is provided, furthermore, the linear multi-objective programming is improved for the whole-optimal land distribution in which the economic effect and redevelopment is considered , and the load of the small area is calculated. Finally, this model is implemented and the analysis result for practical calculation example by this model is satisfied.