An Annual Midterm Energy Forecasting Model Using Fuzzy Logic

• Published in: IEEE transactions on power systems 2009-02, Vol.24 (1), p.469-478
• Main Authors: Elias, C.N., Hatziargyriou, N.D.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Demand forecasting; Economic forecasting; Energy forecasting; Fuzzy logic; Input variables; Job shop scheduling; Load forecasting; optimization of membership functions; Power system analysis computing; Power system modeling; Predictive models; standard deviation
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2008.2009490

The objective of this paper is to present a new fuzzy logic method for midterm energy forecasting. The proposed method properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set and to use a minimal number of patterns. The input variables, the number of the triangular membership functions and their base widths are simultaneously selected by an optimization process. The standard deviation is calculated analytically by mathematical expressions based on the membership functions. Results from an extensive application of the method to the Greek power system and for different categories of customers are compared to those obtained from the application of standard regression methods and artificial neural networks (ANN).