Distributed HS-ARTMAP and its forecasting model for electricity load

• This paper proposes a new network named dHS-ARTMAP based on ARTMAP.
• The method reduces proliferation problems, retains stable memories, and enjoys noise tolerance.
• dHS-ARTMAP model achieves excellent results compared with other models.

Intelligence technology develops quickly to predict and respond to the actions of electric power users to maintain a reliable and secure electricity infrastructure. This paper proposed a new on-line training network called distributed hyper-spherical ARTMAP (dHS-ARTMAP) to forecast the electricity load. The new model constructs a more compact network structure and largely decreases the proliferation problem that Fuzzy ARTMAP models usually encounter. Experiments of short-term electricity load forecasting are made with the data from Queensland, Australia. Results are compared with other methods. The effectiveness of the dHS-ARTMAP network proves itself a promising alternative to put into practical use.

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