Abstract There is a growing interest in the prediction of system marginal price (SMP) in the power pool since electricity industry vesting in England and Wales in 1990. The prediction of SMP improves the financial performance of an independent power producer bidding in the day-ahead market. This paper proposes a neural-network-based approach to predict system marginal price (SMP) with particular reference to weekend and public holidays. The SMP estimator proposed in this paper consists of two phases. One is a front-end processor. The second is a neural-network-based predictor. The raw data are processed by front-end processors which represent the features of SMP on Saturday, Sunday and public holidays, respectively. The output from the front-end-processor forms a neural-network input file. The network is trained by the error backpropagated algorithm. The results show that mean absolute percentage errors of the estimation are 9.40% on Saturday, 8.93% on Sunday and 12.19% on a public holiday, respectively. The program is coded in visual C++ and runs on a PC with friendly windows interface.
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