Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy

In recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach address load forecasting at various levels of disaggregation the Peruvian Interconnected Electrical System (SEIN). The novelty this includes utilizing meteorological data for training, employing an adaptable methodology with easily modifiable internal parameters, achieving low computational cost, and demonstrating high performance terms MAPE. combines modified Fuzzy ARTMAP Neural Network (FAMM) hybrid Support Vector FAMM (SVMFAMM) methods parallel process, using decomposition through Wavelet filter db20. Experimental results show that proposed outperforms state-of-the-art models predicting accuracy across different time intervals.