| Summary: | A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists of rules with dynamic consequent parts that are small-scale recurrent neural networks with one hidden layer, whose neurons have local output feedback. The particular representation maintains the local learning nature of the typical static fuzzy model, since the dynamic consequent parts of the fuzzy rules can be considered as subsystems operating at the subspaces defined by the fuzzy premise parts, and they are interconnected through the defuzzification part. The Greek power system is examined, and hourly based predictions are extracted for the whole year. The recurrent nature of the forecaster leads to the use of a minimal set of inputs, since the temporal relations of the electric load time-series are identified without any prior knowledge of the appropriate past load values being necessary. An extensive simulation analysis is conducted, and the forecaster’s performance is evaluated using appropriate metrics (APE, RMSE, forecast error duration curve). ReNFuzz-LF performs efficiently, attaining an average percentage error of 1.35% and an average yearly absolute error of 86.3 MW. Finally, the performance of the proposed forecaster is compared to a series of Computational Intelligence based models, such that the learning characteristics of ReNFuzz-LF are highlighted.
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