An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network.

Research highlights► The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. ► The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. ► The network on-line forecasts electrical load accurately, stably. ► Both one-period and multi-period load forecasting are executed using data of different cities.