The deregulation of electric power supply industries has raised many challenging problems. One of the most important ones is forecasting the Market Clearing Price (MCP) of electricity. Decisions on various issues, such as to buy or sell electricity and to offer a transaction to the market, require accurate knowledge of the MCP. Another problem, which has also been an important issue of the traditional power systems, is load forecasting for both short and long terms. The extended kalman filter has been widely adopted for state estimation of nonlinear systems, machine learning applications and neural network training. In the EKF, the state distribution is approximated by the first-order linearization of the nonlinear system. Therefore this can introduce large errors in the load and price forecasting as two Chaotic, nonstationary and nonlinear time-series. The unscented Kalman filter (UKF), in contrast, achieves third-order accuracy, by using a minimal set of MCP and load sigma points. In this paper an improved dual unscented Kalman filter (DUKF), which estimate state and parameter simultaneously has been applied to the real New England power market. The numerical stability and more accurate predictions of our method is comparable to the EKF, and traditional neural network training methods. Remarkably, the computational complexity of the DUKF is the same order as that of the EKE. The obtained results show significant improvement in both price and load forecasting.

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