Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture (adaptive resonance theory), named fuzzy ART&ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ART architecture have two fundamental characteristics that are extremely important for the network performance (stability and plasticity), which allow the implementation of continuous training. The fuzzy ART&ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART&ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used.