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. In this paper, a new forecasting method is introduced to predict the next day electricity price and load. The proposed method is based on cooperative co-evolutionary (Co-Co) approach and has been applied to the real power market. Most of the conventional forecasting methods are based on a single neural network prediction. These methods might misrepresent parts of the input–output data mapping that could have been correctly represented by cooperation of multiple networks. In this paper, a new Co-Co adaptive algorithm with adjustable connections in a recursive procedure is proposed. The obtained results show significant improvement in both price and load forecasting.

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