Fuzzy Models for Short Term Power Forecasting in Palestine

Raed Basbous

doi:10.48161/qaj.v3n4a268

Authors

Raed Basbous Al-Quds Open University, Palestine

DOI:

https://doi.org/10.48161/qaj.v3n4a268

Keywords:

Subtractive Clustering, Fuzzy Logic, Sugeno, ANFIS, Neural Networks, Hybrid Optimization

Abstract

Short-Term Load Forecasting (STLF) is needed to efficiently manage the power systems. In this paper, two kinds of models that depend on the Fuzzy based techniques are developed to represent the STLF models in Palestine. Different types of these models have been developed using the available data sets that include the past electric load values and the climatic variables as inputs. It is shown that the climatic variables have a major effect on the predicted load. Various optimization techniques are used to develop the proposed models including hybrid and Backpropagation optimization techniques, Subtractive Clustering, and combining the Subtractive Clustering and Hybrid optimization techniques. The obtained results indicate the efficiency of the proposed models using the time and weather data.

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