Locational marginal price forecasting in deregulated electric markets using a recurrent neural network

Recently, deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. Locational marginal prices (LMPs) resulting from bidding competition signal electricity values at a node or in an area. This paper presents a method using recurrent neural networks (RNNs) for forecasting LMPs. These RNNs were trained/validated and tested with historical data from the PJM power system. It was found that the proposed neural networks are capable of forecasting LMP values efficiently.